Papers by Lei Lei
Be Your Own Red Teamer: Safety Alignment via Self-Play and Reflective Experience Replay (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have remarkable capabilities but are vulnerable to adversarial “jailbreak” attacks designed to bypass safety guardrails. |
| Approach: | They propose to empower a large language model to be its own red teamer . safety self-play allows the model to act as both the Attacker and Defender . |
| Outcome: | The proposed approach outperforms baselines trained on static adversarial datasets and establishes a new benchmark for proactive safety alignment. |
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder (2022.coling-1)
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| Challenge: | Existing methods for numerical reasoning are not flexible enough to handle diverse expressions. |
| Approach: | They propose a Relational Graph enhanced Hybrid table-text Numerical reasoning model with Tree decoder which captures relationship between numerical value, table schema, and text information on the encoder side. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on the publicly available tabletext hybrid QA benchmark. |
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge (2023.acl-long)
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| Challenge: | Large language models (LLMs) have been studied for their ability to store and utilize positive knowledge. |
| Approach: | They propose to use a constrained keywords-to-sentence generation task and a Boolean question answering task to probe large language models on negative commonsense knowledge. |
| Outcome: | The proposed tasks show that LLMs fail to generate valid sentences grounded in negative commonsense knowledge, yet they can correctly answer yes-or-no questions. |
TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions, but they are limited by noise and e.g., users may click questions they don't like, leading to inaccurate semantics modeling. |
| Approach: | They propose to introduce tags of FAQ questions to reduce noise in the conversation context and integrate them into a reinforcement learning framework to minimize the negative impact of irrelevant information. |
| Outcome: | The proposed method can eliminate irrelevant information and minimize negative impact of irrelevant information in the dynamic conversation context. |
RiOT: Efficient Prompt Refinement with Residual Optimization Tree (2025.acl-long)
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| Challenge: | Existing methods for automatic prompt optimization face two challenges: lack of diversity and semantic drift. |
| Approach: | They propose a framework for automatic prompt optimization that iteratively refines prompts through text gradients and selects the best prompt using perplexity. |
| Outcome: | The proposed framework outperforms existing prompt optimization methods and manual prompting on commonsense, mathematical, logical, temporal, and semantic reasoning benchmarks. |
CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning (2023.acl-long)
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| Challenge: | HKUST-KnowComp proposes a framework for commonsense reasoning that can be used to conceptualize commonsence knowledge bases at scale. |
| Approach: | They propose a framework that integrates event conceptualization and instantiation to conceptualize commonsense knowledge bases at scale. |
| Outcome: | The proposed framework achieves state-of-the-art on two conceptualization tasks and the acquired abstract commonsense knowledge significantly improves commonsence inference modeling. |
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma GongQue, Shanglin Lei, YiFan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, Honggang Zhang
| Challenge: | Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization. |
| Approach: | They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation. |
| Outcome: | The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts. |
How LLMs React to Industrial Spatio-Temporal Data? Assessing Hallucination with a Novel Traffic Incident Benchmark Dataset (2025.naacl-industry)
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Qiang Li, Mingkun Tan, Xun Zhao, Dan Zhang, Daoan Zhang, Shengzhao Lei, Anderson S. Chu, Lujun Li, Porawit Kamnoedboon
| Challenge: | Large language models (LLMs) have a high potential to digitize and enhance the health & public services industry. |
| Approach: | They propose to use a cross-lingual benchmark dataset to assess the robustness of state-of-the-art LLMs in the spatio vs temporal domain for traffic incident classification. |
| Outcome: | The proposed model performs well in the spatio-temporal domain and in the non-English context. |
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations (2024.findings-emnlp)
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| Challenge: | Existing CRSs can be highly persuasive, but they can be deceptive and can damage the long-term trust between users and the CRS. |
| Approach: | They propose a method to enhance the credibility of CRS’s explanations by using a set of credibility-aware persuasive strategies and a post-hoc self-reflection process. |
| Outcome: | The proposed method enhances the credibility of CRS’s explanations and refines them via post-hoc self-reflection. |
ASPO: Adaptive Sentence-Level Preference Optimization for Fine-Grained Multimodal Reasoning (2025.findings-acl)
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| Challenge: | Recent advances have extended DPO to multimodal scenarios, achieving strong performance. |
| Approach: | They propose to use a sentence-level preference optimization technique to optimize individual sentences for more precise preference optimization without additional models or parameters. |
| Outcome: | Experiments show that Adaptive Sentence-level Preference Optimization significantly improves the alignment of multimodal models. |
CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds (2025.naacl-long)
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| Challenge: | Evaluating role-playing capabilities in large language models is challenging due to complex dynamics involved in role-playering. |
| Approach: | They propose a simulation sandbox that generates situational fine-grained character behavior trajectories to enhance LLM performance. |
| Outcome: | The proposed model generates situational fine-grained character behavior trajectories to enhance performance. |
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures (P18-1)
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| Challenge: | Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces complexity and fragility. |
| Approach: | They propose a novel sequence-to-sequence (seq2sequ) model which tracks dialogue believes and a two stage copynet instantiation which emonstrates good scalability. |
| Outcome: | The proposed framework outperforms state-of-the-art pipeline-based methods on large datasets and retains satisfactory entity match rate on out-of vocabulary (OOV) cases where pipeline-designed competitors totally fail. |
Ethos: Rectifying Language Models in Orthogonal Parameter Space (2024.findings-naacl)
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| Challenge: | Language models (LMs) generate toxic, biased content and reveal private training records. |
| Approach: | They propose an efficient approach that rectifies LMs to mitigate toxicity and bias . Ethos distinguishes general beneficial and undesired knowledge when reconstructing task vectors . |
| Outcome: | The proposed approach mitigates toxicity and bias in outputs and avoids privacy leakage. |
mTVR: Multilingual Moment Retrieval in Videos (2021.acl-short)
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| Challenge: | mTVR is a multilingual video moment retrieval dataset with 218K queries in English and Chinese . Various datasets have been proposed or adapted for the task, but they are all created for a single language (English). |
| Approach: | They propose a multilingual video moment retrieval dataset with 218K queries from 21.8K TV show video clips. |
| Outcome: | The proposed model outperforms strong monolingual baselines while using fewer parameters. |
OpenForecast: A Large-Scale Open-Ended Event Forecasting Dataset (2025.coling-main)
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| Challenge: | Existing closed-ended event forecasting methods are constrained by a limited answer space. |
| Approach: | They introduce OpenForecast, a large-scale open-ended dataset with three open-ending event forecasting tasks and an automatic LLM-based method for complex events. |
| Outcome: | The proposed method can be used to evaluate the ability of complex event forecasting of large language models. |
DiaLLMs: EHR-Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction (2025.findings-acl)
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| Challenge: | Existing medical LLMs focus primarily on diagnosis recommendation, limiting their clinical applicability. |
| Approach: | They propose a medical LLM that integrates heterogeneous EHR data into clinically grounded dialogues. |
| Outcome: | The proposed model outperforms baselines in clinical test recommendation and diagnosis prediction. |
Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond (2023.acl-tutorials)
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| Challenge: | Conventional conversation researches focus on the responseability of the system, such as dialogue context understanding and response generation, but overlook the design of an essential property in intelligent conversations, i.e., goal awareness. |
| Approach: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
| Outcome: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)
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Shuang Peng, Mengdi Zhou, Minghui Yang, Haitao Mi, Shaosheng Cao, Zujie Wen, Teng Xu, Hongbin Wang, Lei Liu
| Challenge: | a new system that integrates advanced NLP technologies for medical insurance assessment is proposed . the average time cost of the procedure is reduced from 55 minutes to 35 minutes . |
| Approach: | They propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. |
| Outcome: | The proposed system reduces the time cost of the procedure from 55 minutes to 35 minutes and saves 30% human resources cost compared with the previous offline procedure. |
Diverse Pretrained Context Encodings Improve Document Translation (2021.acl-long)
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| Challenge: | Existing models for sentence-level sequence-to-sequence translations do not use extra-sentential information. |
| Approach: | They propose a sentence-level sequence-to-sequence transformer with multiple pre-trained context signals. |
| Outcome: | The proposed model outperforms existing models on Chinese-English and English-German tasks. |
NeurST: Neural Speech Translation Toolkit (2021.acl-demo)
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| Challenge: | a toolkit for speech translation is available for free and provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. |
| Approach: | They propose to use NeurST to facilitate speech translation research for NLP researchers . they show experimental results for different benchmark datasets which can be regarded as reliable baselines . |
| Outcome: | The proposed framework provides reliable benchmarks for speech translation research. |
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory (2025.findings-emnlp)
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Yihang Wang, Xu Huang, Bowen Tian, Yueyang Su, Lei Yu, Huaming Liao, Yixing Fan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods for compressing context by removing redundant tokens are inconsistent with the objective of retaining the most important tokens when conditioning on a given query. |
| Approach: | They propose a method that uses information bottleneck theory to compress context . they propose to remove redundant tokens using metrics such as self-information or perplexity . |
| Outcome: | The proposed method achieves a 25% increase in compression rate compared to the state-of-the-art . |
Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction (2025.coling-main)
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| Challenge: | Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text. |
| Approach: | They propose a Fusion Selection-Generation-Based Approach that merges selective and generative methods to enhance argument extraction accuracy. |
| Outcome: | The proposed method improves on the RAMS and WikiEvents, while preserving the unique characteristics of both methods. |
Too Long, Do Re-weighting for Efficient LLM Reasoning Compression (2026.acl-long)
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Zhong-Zhi Li, Xiao Liang, Zihao Tang, Lei Ji, Peijie Wang, Haotian Xu, Xing W, Haizhen Huang, Weiwei Deng, Yeyun Gong, Zhijiang Guo, Xiao Liu, Fei Yin, Cheng-Lin Liu
| Challenge: | Large Language Models (LLMs) have recently achieved remarkable progress on complex reasoning tasks by leveraging extended Chain-of-Thought (CoT) techniques. |
| Approach: | They propose a method that uses Extended Chain-of-Thought (EFT) to reduce the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. |
| Outcome: | The proposed method reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. |
Lost in Decomposition: Analyzing and Mitigating the Limitations of Long Context Methods via Context Dependency (2026.findings-acl)
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| Challenge: | Existing workflow-based long context methods do not perform well on specific datasets . performance degradation is associated with the indiscriminate application of long context models . |
| Approach: | They propose a training-free adaptive routing strategy to improve long context large language models' robustness. |
| Outcome: | The proposed method can be generalized to all types of datasets, but performance degradation is a concern. |
Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback (2025.acl-long)
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Jiakang Yuan, Xiangchao Yan, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou
| Challenge: | Recent studies show that AI-assisted research methods can improve research efficiency . a closed-loop framework is used to enhance the automation level of scientific research . |
| Approach: | They propose a closed-loop LLM-driven framework to enhance the automation level of scientific research. |
| Outcome: | The proposed framework improves the efficiency of scientific research by improving data analysis, accelerating computation, and fostering novel idea generation. |
CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)
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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap . |
| Approach: | They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models. |
| Outcome: | The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting . |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities (2023.findings-eacl)
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Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei, Lizhen Qu, Gerard de Melo, Xiaojun Chang, Yazhou Ren, Zenglin Xu
| Challenge: | a vision-language benchmark for human activity planning is designed for humans . the task is easy for humans, but challenging for SOTA deep learning models . |
| Approach: | They propose a vision-language benchmark for human activity planning that extends Charades with intents and builds on a multi-choice question test set. |
| Outcome: | The proposed benchmark evaluates the ability of systems to anticipate and plan human actions in a multimodal visionlanguage setting. |
ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms (2022.findings-emnlp)
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| Challenge: | Existing unsupervised methods for paraphrase generation are weak in semantic equivalence or expression diversity. |
| Approach: | They propose a framework for unsupervised paraphrase generation that employs multi-aspect equivalence constraints and multi-granularity diversifying mechanisms to achieve good semantic equvalence and expressive diversity. |
| Outcome: | The proposed framework achieves 9.1% and 3.3% absolute gains over previous SOTA on Quora and MSCOCO and can improve to 18.0% and 4.6% on GLUE. |
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification (2022.lrec-1)
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| Challenge: | Recent advances in natural language processing (NLP) have reshaped the industry . complexity of such models makes them a “black box” and can cause ethical concerns . |
| Approach: | They propose a convolutional TM architecture that breaks down text into a sequence of fragments . they propose to use a tokenization scheme to bind the tokens to the text fragments. |
| Outcome: | The proposed architecture improves on a set of text fragments and eliminates the need for a corpus-specific vocabulary. |
Corpus-Steered Query Expansion with Large Language Models (2024.eacl-short)
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| Challenge: | Recent studies show query expansions generate hypothetical documents that answer queries as expansions. |
| Approach: | They propose a corpus-steered query expansion to promote incorporation of knowledge embedded within the corpus. |
| Outcome: | et al. analyzed corpus-based Query Expansion (CSQE) using LLMs to generate hypothetical documents that answer the query. |
MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)
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Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu
| Challenge: | Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning. |
| Approach: | They propose a multimodal scientific dataset and benchmark curated from open-access publications. |
| Outcome: | MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers. |
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)
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Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
| Challenge: | Large language models exhibit significant performance discrepancies between high- and low-resource languages. |
| Approach: | They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset. |
| Outcome: | The proposed model achieves consistent multilingual representations across languages. |
Dialog-Post: Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training (2023.acl-long)
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| Challenge: | a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks . |
| Approach: | They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively . |
| Outcome: | The proposed method outperforms existing models and achieves a 3.3% improvement on average. |
Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints (2023.acl-long)
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| Challenge: | Existing attempts to generate similes as context-free tasks are not suitable for simile generation . however, simile generated under such settings might be undesirable, we argue . |
| Approach: | They propose a model to generate a simile with multiple simile elements . they propose to use a vehicle retrieval module to obtain the explicable comparison . |
| Outcome: | The proposed model can generate a simile with multiple simile elements, e.g., context and vehicle. |
Automatic Table Union Search with Tabular Representation Learning (2023.findings-acl)
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Xuming Hu, Shen Wang, Xiao Qin, Chuan Lei, Zhengyuan Shen, Christos Faloutsos, Asterios Katsifodimos, George Karypis, Lijie Wen, Philip S. Yu
| Challenge: | Existing methods to identify uniability based on column representations are insufficient to reveal latent relational features to describe column relation between pair of columns. |
| Approach: | They propose a self-supervised table union search framework called AutoTUS to learn column relational representations in a multi-stage manner. |
| Outcome: | The proposed framework improves on the SOTA baseline and on real-world datasets. |
TWAG: A Topic-Guided Wikipedia Abstract Generator (2021.acl-long)
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| Challenge: | Existing models view Wikipedia abstract as plain text, ignoring that it is a description of a certain entity and can be decomposed into different topics. |
| Approach: | They propose a model that guides Wikipedia abstract generation with topical information. |
| Outcome: | The proposed model outperforms baselines and is capable of generating comprehensive abstracts. |
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)
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| Challenge: | Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge. |
| Approach: | They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms. |
| Outcome: | The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors. |
LightSeq: A High Performance Inference Library for Transformers (2021.naacl-industry)
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| Challenge: | Existing inference frameworks for natural language processing are not the best choice for online service of sequence processing problems. |
| Approach: | They propose a highly efficient inference library for Transformer models that includes GPU optimization techniques to streamline computation and reduce memory footprint. |
| Outcome: | The proposed library achieves 14x speedup compared with TensorFlow and 1.4x speed up compared to a concurrent CUDA implementation. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)
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| Challenge: | Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations. |
| Approach: | They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy . |
| Outcome: | The proposed framework outperforms baseline methods on six benchmark datasets. |
Scaling LLM Inference Efficiently with Optimized Sample Compute Allocation (2025.naacl-long)
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| Challenge: | Existing methods to optimize sample allocations for large language models fail to account for the optimal sampling configuration. |
| Approach: | They propose an algorithm that optimizes sample allocation by finding an optimal mix of different inference configurations. |
| Outcome: | The proposed algorithm achieves better accuracy on SWE-Bench with 3x less compute than the default configuration. |
RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods fail to model non-commutative composition patterns . Existing methods are limited to complex space, resulting in a large number of parameters. |
| Approach: | They propose a knowledge graph embedding method that transforms the coordinates of each entity and then represents each relation as a rotation from head entity to tail entity in complex space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on link prediction and path query answering. |
LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)
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| Challenge: | Existing supervised learning methods in natural language processing require large amounts of data. |
| Approach: | They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently. |
| Outcome: | The proposed model outperforms existing models with few-shot performance in two NLP tasks. |
IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)
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| Challenge: | Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities . |
| Approach: | They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens. |
| Outcome: | The proposed model improves the performance of LLMs for IE with zero-shot generalization. |
LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)
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| Challenge: | Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus. |
| Approach: | They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders. |
| Outcome: | The proposed framework extracts Chinese and English linguistic features across four dimensions . it uncovers intrinsic representations of linguistic knowledge in LLMs and can control outputs . |
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts (2026.acl-long)
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Sijia Luo, Xiaokang Zhang, Yuxuan Hu, Bohan Zhang, Ke Wang, Jinbo Su, Mengshu Sun, Lei Liang, Jing Zhang
| Challenge: | Existing methods for storing key-value caches during long-horizon rollouts cause performance collapses. |
| Approach: | They propose a new training paradigm that empowers stable RL training under sparse rollouts. |
| Outcome: | The proposed model reduces rollout overhead while maintaining the performance. |
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (D18-1)
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| Challenge: | Existing methods for learning word and entity representations in monolingual settings are limited. |
| Approach: | They propose a method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge and enables cross-linguistic inferences. |
| Outcome: | The proposed method captures mutually complementary knowledge and enables cross-lingual inferences among knowledge bases and texts. |
Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking (2025.emnlp-main)
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| Challenge: | Large Reasoning Models (LLMs) have demonstrated impressive performances across diverse domains, but how their safety benefits from enhanced reasoning capabilities against jailbreak queries remains unexplored. |
| Approach: | They propose a safety-aware reasoning paradigm that integrates a pivot token-based safety-based reasoning mechanism into LLMs’ generation process. |
| Outcome: | The proposed model improves the safety of large language models against jailbreak queries while minimizing attacks and maintaining the original performance. |
Large Language Models are not Fair Evaluators (2024.acl-long)
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Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, Zhifang Sui
| Challenge: | Existing evaluation frameworks that use large language models as referees are insufficient for accurately assessing their alignment with human intent. |
| Approach: | They propose a calibration framework to address positional bias in large language models as evaluators by manually annotating the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicun A Benchmark’s question prompt. |
| Outcome: | The proposed framework alleviates evaluation bias, resulting in closer alignment with human judgments. |
DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections (2026.findings-acl)
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| Challenge: | Several studies rely on additional models to optimize mixtures. |
| Approach: | They propose a method that dynamically optimizes instruction-tuning dataset mixtures by prior-scaled Boltzmann Exploration and a multi-armed bandit setup. |
| Outcome: | The proposed method improves the TÜLU-2-mixture and TÜLO-3-mixtures across 10 benchmarks while introducing minimal computational overhead over naive sampling. |
RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models (2025.emnlp-main)
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| Challenge: | Current temporal knowledge graph question answering methods focus on implicit temporal constraints and lack the capability to handle complex temporal queries. |
| Approach: | They propose a temporal knowledge graph question answering framework that recursively decomposes questions into sub-problems and employs multi-path answer aggregation to improve fault tolerance. |
| Outcome: | The proposed framework outperforms existing methods on multiTQ and TimelineKGQA benchmarks. |
PVTNL: Prompting Vision Transformers with Natural Language for Generalizable Person Re-identification (2025.findings-emnlp)
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| Challenge: | Domain generalization person re-identification (DG-ReID) aims to train models on source domains and generalize to unseen target domains. |
| Approach: | They propose a framework to generalize person re-identification using a vision-language model . body-part cues are used to segment images into semantically coherent regions . |
| Outcome: | The proposed framework can generalize to unseen domains and generalize semantics to people . it leverages the pre-trained vision-language model BLIP to extract aligned visual and textual embeddings. |
Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)
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| Challenge: | Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC. |
| Approach: | They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score. |
Improving Event Detection via Open-domain Trigger Knowledge (2020.acl-main)
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| Challenge: | Existing methods for event detecting are prone to overfitting densely labeled trigger words due to the small scale of training data. |
| Approach: | They propose a novel Enrichment Knowledge Distillation model to leverage external open-domain trigger knowledge to reduce in-built biases to frequent trigger words in annotations. |
| Outcome: | The proposed model outperforms nine strong baselines and is especially effective for unseen/sparsely labeled trigger words. |
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA (2025.findings-acl)
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Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li
| Challenge: | Current long-context large language models lack citations to support their responses, making verification difficult due to potential hallucinations. |
| Approach: | They propose to use off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations and leverage this pipeline to construct a large-scale SFT dataset for LQAC. |
| Outcome: | The proposed pipeline can generate responses with fine-grained citations on the fly, surpassing existing models including GPT-4o. |
Stimulate the Critical Thinking of LLMs via Debiasing Discussion (2025.emnlp-main)
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| Challenge: | Existing studies show that large language models (LLMs) are often prone to stance homogeneity and human preference biases when faced with conflicting perspectives. |
| Approach: | They propose a novel two-stage training framework to address stance homogeneity bias and human preference bias by generating multi-model discussion datasets and optimizing reinforcement learning from human feedback to align with discussion correctness. |
| Outcome: | The proposed framework reduces stance homogeneity bias and human preference bias and improves generalization capabilities on non-discussion scenarios and out-of-domain datasets. |
Revealing Single Frame Bias for Video-and-Language Learning (2023.acl-long)
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| Challenge: | Existing methods for video-and-language learning use multiple frames as inputs. |
| Approach: | They propose to use single-frame models for video-and-language learning to investigate temporality in video- and language tasks. |
| Outcome: | The proposed model does not take into account temporal information on video-and-language tasks. |
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction (2026.findings-acl)
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| Challenge: | Muon’s orthogonalized update rule suppresses the emergence of heavy-tailed weight spectra and over-emphasizes the training along noise-dominated directions. |
| Approach: | They propose to preserve Muon's ability to capture parameter interdependencies while producing heavier-tailed updates and inducing heavier-tail weight spectra. |
| Outcome: | The proposed algorithm suppresses the emergence of heavy-tailed weight spectra and over-emphasizes training along noise-dominated directions. |
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown nearly saturated performance on many NLP tasks. |
| Approach: | They construct multiple sensitive factors time QA which encompasses three temporal factors . they test current mainstream LLMs with different parameter sizes . |
| Outcome: | The proposed model incorporates three temporal factors with 2,853 samples . the results show that LLMs fall behind smaller models on these factors . |
CCSRD: Content-Centric Speech Representation Disentanglement Learning for End-to-End Speech Translation (2023.findings-emnlp)
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| Challenge: | Existing speech-to-text translation models can extract features from speech inputs, but they may include non-linguistic speech factors such as pitch, timbre and speaker identity. |
| Approach: | They propose a content-centric speech representation disentanglement learning framework for speech translation that decomposes speech representations into content representations and non-linguistic representations via representation disentanglement learning. |
| Outcome: | The proposed framework outperforms state-of-the-art speech translation models and cascaded models on five translation directions. |
On the Sentence Embeddings from Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Pre-trained contextual representations like BERT have been widely used for NLP tasks. |
| Approach: | They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective. |
| Outcome: | The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks. |
Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding (2025.findings-naacl)
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| Challenge: | Existing methods for integrating knowledge graphs rely on entity and relation embeddings . Fig. 1 shows how to decode knowledge graph in under 6 seconds . |
| Approach: | They propose a framework that only utilizes entity embeddings to decode knowledge graphs. |
| Outcome: | The proposed framework reconstructs KG representation by maximizing smoothness of entity embeddings. |
Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)
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| Challenge: | Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed . |
| Approach: | They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents. |
| Outcome: | The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets. |
MentalSeek-Dx: Towards Progressive Hypothetico-Deductive Reasoning for Real-world Psychiatric Diagnosis (2026.acl-long)
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Xiao Sun, null Ymyang, Xinyi Jiang, Yu Tian, Junnan Zhu, Jiang Zhong, Qin Lei, Jingwang Huang, Haoyang Zeng, Xinyu Zhou, Xin Xiao, Kaiwen Wei
| Challenge: | Mental health disorders represent a burgeoning global public health challenge . lack of ecological validity and fine-grained diagnostic supervision limits their utility . |
| Approach: | They propose a medical-specialized LLM trained to internalize clinical reasoning process through supervised trajectory construction and curriculum-based reinforcement learning. |
| Outcome: | The proposed model achieves state-of-the-art with only 14B parameters, establishing a clinically grounded framework for reliable psychiatric diagnosis. |
Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)
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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei
| Challenge: | Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts. |
| Approach: | They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis. |
| Outcome: | The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents. |
Hashing based Efficient Inference for Image-Text Matching (2021.findings-acl)
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| Challenge: | Recent work on image-text matching has focused on exploring interactions between images and sentences to improve performance without considering inference efficiency. |
| Approach: | They propose a hashing-based efficient inference module which can be plugged into existing frameworks to speed up inference step without reducing retrieval performance. |
| Outcome: | The proposed module can be plugged into existing framework to speed up inference step without reducing retrieval performance. |
GMH: A General Multi-hop Reasoning Model for KG Completion (2021.emnlp-main)
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| Challenge: | Knowledge graphs are incomplete with many facts missing, causing performance bottlenecks in many applications. |
| Approach: | They propose a general multi-hop reasoning task that can be formulated as a search process and can be extended to long-distance reasoning scenarios. |
| Outcome: | The proposed model improves on baselines in short and long distance reasoning scenarios. |
Learning Personalized Alignment for Evaluating Open-ended Text Generation (2024.emnlp-main)
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| Challenge: | Traditional evaluation metrics rely heavily on lexical similarity with human-written references, showing poor correlation with human judgments and failing to account for alignment with the diversity of human preferences. |
| Approach: | They propose an interpretable evaluation framework that evaluates alignment with specific human preferences by providing detailed comments and fine-grained scoring. |
| Outcome: | The proposed framework outperforms GPT-4 in Kendall correlation and accuracy with zero-shot reviewers. |
Learning When to Translate for Streaming Speech (2022.acl-long)
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| Challenge: | Existing methods waiting-and-translating for a fixed duration break speech acoustic units . Existing models waiting-for a set duration and generating partial sentences are not effective . |
| Approach: | They propose a monotonic segmentation module inside an encoder-decoder model to detect proper speech unit boundaries for a streaming speech input. |
| Outcome: | The proposed method outperforms existing methods on a speech translation dataset and achieves the best trade-off between translation quality and latency. |
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)
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| Challenge: | Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations . |
| Approach: | They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios. |
| Outcome: | The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL. |
CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)
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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin
| Challenge: | Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences. |
| Approach: | They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks. |
| Outcome: | The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones. |
A State-transition Framework to Answer Complex Questions over Knowledge Base (D18-1)
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| Challenge: | Existing methods for complex question answering have some limitations . existing methods employ predefined patterns or templates to understand complex questions. |
| Approach: | They propose a state transition-based approach to translate a natural language question to a semantic query graph. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on several benchmarks with two knowledge bases. |
OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding (2023.emnlp-demo)
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| Challenge: | Event understanding is fundamental for humans to understand the world. |
| Approach: | They propose an event understanding toolkit called OmniEvent that is comprehensive and fair . it supports mainstream modeling paradigms and the processing of 15 widely-used datasets . |
| Outcome: | The toolkit supports mainstream modeling paradigms and the processing of 15 widely-used English and Chinese datasets. |
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models (2024.acl-long)
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| Challenge: | Recent studies have developed watermarking algorithms which restrict the generation process to leave an invisible trace for watermark detection. |
| Approach: | They propose a benchmarking procedure that compares different methods to ensure consistent watermarking strength and jointly evaluates their generation and detection performance. |
| Outcome: | The proposed benchmark compares 4 open-source watermarks on 2 LLMs under 2 watermarking strengths and observes the common struggles for current methods on maintaining the generation quality. |
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information (P19-1)
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| Challenge: | Existing work on commenting based on textual content is focused on other modalities, such as graphics and images. |
| Approach: | They propose a task to integrate multiple modalities into automatic commenting . they construct a large-scale dataset and propose 'co-attention' model to capture dependency between textual and visual information. |
| Outcome: | The proposed model can achieve better performance than baselines. |
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)
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| Challenge: | Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities. |
| Approach: | They propose a framework that integrates emotion correlation learning, utilization, and supervising. |
| Outcome: | The proposed framework improves empathetic perception and expression on a humanized dialogue dataset. |
Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues (2024.emnlp-main)
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| Challenge: | Current research treats personality recognition as a classification task, failing to reveal the supporting evidence for the recognized personality. |
| Approach: | They propose a task that aims to reveal the reasoning process as supporting evidence of the personality trait. |
| Outcome: | The proposed task reveals the reasoning process as supporting evidence of the personality trait. |
HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment (2026.findings-acl)
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| Challenge: | Existing methods for enhancing sequential recommendation use long interaction sequences, but they lack the ability to extract user preferences from long sequences. |
| Approach: | They propose a plugin that integrates LLMs to infer user preferences from interaction sequences. |
| Outcome: | The proposed algorithms improve user semantic embedding extraction and utilization on three benchmark datasets. |
Mining the Past with Dual Criteria: Integrating Three types of Historical Information for Context-aware Event Forecasting (2025.emnlp-main)
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Rong Ma, Lei Wang, Yating Yang, Bo Ma, Rui Dong, Fengyi Yang, Ahtamjan Ahmat, Kaiwen Lu, Xinyue Wang
| Challenge: | Existing methods focus on entities and structural dependencies but overlook implicitly relevant information. |
| Approach: | They propose a method that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. |
| Outcome: | The proposed method outperforms existing methods on three public benchmark datasets and is highly effective on two structured temporal knowledge graph forecasting datasets. |
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news. |
| Approach: | They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately. |
| Outcome: | The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets. |
Adversarial Domain Adaptation for Duplicate Question Detection (D18-1)
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| Challenge: | Recent years have seen the rise of community question answering forums . duplicate questions easily become ubiquitous as users often ask the same question, possibly in a slightly different formulation, making it difficult to find the best (or one correct) answer. |
| Approach: | They propose to use domain adaptation to detect duplicate questions in forums . they find that domain adaptation improves performance over multiple pairs of domains . |
| Outcome: | The proposed approach improves 5.6% over the best baseline across multiple pairs of domains. |
LLM4Vis: Explainable Visualization Recommendation using ChatGPT (2023.emnlp-industry)
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| Challenge: | Existing methods to perform visualization recommendation require a large corpus of dataset-visualization pairs for training and lack natural explanations for their results. |
| Approach: | They propose a new method that uses a ChatGPT-based prompting approach to perform visualization recommendation and return human-like explanations using very few demonstration examples. |
| Outcome: | The proposed method outperforms or performs similarly to supervised learning models like Random Forest, Decision Tree, and MLP, in both few-shot and zero-shot settings. |
XDoc: Unified Pre-training for Cross-Format Document Understanding (2022.findings-emnlp)
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| Challenge: | Existing pre-trained models target one document format at a time, making it difficult to combine knowledge from multiple document formats. |
| Approach: | They propose a unified pre-trained model which deals with different document formats in a single model. |
| Outcome: | The proposed model achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models. |
Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (2025.acl-long)
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| Challenge: | Existing retrieval-augmented generation methods are insufficient for multi-hop question answering . however, they tend to generate hallucinations due to semantic mismatching . |
| Approach: | They propose to optimize question semantic space for dynamic retrieval-augmented multi-hop question answering by optimizing the semantic embeddings. |
| Outcome: | The proposed method outperforms existing RAG methods in both in- and out-of-domain settings. |
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
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| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |
DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton
| Challenge: | commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools . |
| Approach: | They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression . |
| Outcome: | The proposed approach outperforms human experts in medical examinations on diverse datasets. |
Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains (2026.findings-acl)
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| Challenge: | a single transition error can propagate through the entire reasoning chain, leading to unstable performance. |
| Approach: | They propose a framework that intervenes at logical connective junctions to improve LLMs' reasoning. |
| Outcome: | The proposed framework achieves favorable accuracy–efficiency trade-off compared to global inference time scaling methods like beam search and self-consistency. |
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)
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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
| Challenge: | Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios. |
| Approach: | They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers . |
| Outcome: | The proposed model outperforms baselines and class transfer models in low-resource scenarios. |
Translating a Math Word Problem to a Expression Tree (D18-1)
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| Challenge: | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. |
| Approach: | They propose an equation normalization method to normalize duplicated equations and propose an ensemble model to combine their advantages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on the math word problem solving. |
Discreteness in Neural Natural Language Processing (D19-2)
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| Challenge: | This tutorial provides a comprehensive guide to the process of discreteness in neural NLP. |
| Approach: | This tutorial provides a comprehensive guide to the process of discreteness in neural NLP. |
| Outcome: | This tutorial explains the process of discreteness in neural NLP. |
Nutri-bullets Hybrid: Consensual Multi-document Summarization (2021.naacl-main)
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| Challenge: | Existing methods for generating comparative summaries that highlight similarities and contradictions in input documents are lacking large parallel training data for their training. |
| Approach: | They propose a method for generating comparative summaries that highlight similarities and contradictions in input documents by using a neural interpretation of traditional concept-to-text generation systems. |
| Outcome: | The proposed model is compared with conventional methods in the domain of nutrition and health, where the existing models lack large parallel training data. |
Intrinsic Test of Unlearning Using Parametric Knowledge Traces (2025.emnlp-main)
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| Challenge: | Existing methods for “unlearning” information captured in large language models rely on behavioral tests without monitoring residual knowledge in model parameters. |
| Approach: | They propose a general evaluation methodology that uses vocabulary projections to inspect concepts encoded in model parameters. |
| Outcome: | The proposed method detects changes in parametric traces of unlearned concepts and localizes them in two open-source LLMs. |
Unsupervised Multi-scale Expressive Speaking Style Modeling with Hierarchical Context Information for Audiobook Speech Synthesis (2022.coling-1)
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| Challenge: | a recent study has shown that expressiveness of audiobooks is limited by the averaged global-scale speaking style representation. |
| Approach: | They propose an unsupervised multi-scale context-sensitive text-to-speech model for audiobooks . they use hierarchical context encoder to predict global-scale contextual style embeddings . |
| Outcome: | The proposed model outperforms existing models on a real-world Mandarin audio dataset. |
Personalized Transformer for Explainable Recommendation (2021.acl-long)
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| Challenge: | Recent years have witnessed the successful application of natural language generation. |
| Approach: | They propose a model that uses user and item IDs to predict the words in the target explanation to make personalized Transformer. |
| Outcome: | The proposed model outperforms BERT on the explainable recommendation task in terms of effectiveness and efficiency. |
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL Generation (2020.coling-main)
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| Challenge: | Existing approaches to text-to-SQL generation depend on interaction history and current utterances. |
| Approach: | They propose an encoder-decoder model based on interaction-level encoder to capture historical information of SQL query and reuse the previous SQL query tokens. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model on the SParC benchmark . it achieves 34.0% question matching accuracy and 19.0% interaction matching accuracy . |
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)
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| Challenge: | Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK. |
| Approach: | They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms. |
| Outcome: | The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units . |
Learning Language Specific Sub-network for Multilingual Machine Translation (2021.acl-long)
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| Challenge: | Multilingual neural machine translation models suffer from performance degradation when learning multiple languages. |
| Approach: | They propose to use LaSS to jointly train a single unified multilingual MT model. |
| Outcome: | The proposed model gains on 36 language pairs by up to 1.2 BLEU and zero-shot translation with 8.3 BLUE on 30 language pairs. |
Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)
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Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang
| Challenge: | Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability. |
| Approach: | They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action . |
| Outcome: | The proposed agent improves 19.8% over baselines on complex questions and multi-tasks. |
LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN (2026.findings-acl)
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Sijie Wang, Kai Zhao, Wee Peng Tay, Shuo Zhang, Chengwen Liu, Quanjiang Guo, Ren Junhao, Xin Li, Heng Lian, Jingdi Lei, Rui She, Huacan Wang, Ronghao Chen
| Challenge: | Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding. |
| Approach: | They propose an SWE-level benchmark for AI coding in the Huawei Ascend CANN software stack. |
| Outcome: | The proposed benchmark is constructed from real-world CANN repositories and consists of over 400 task instances spanning multiple file, multi-language, and execution-aware coding challenges. |
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)
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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
| Challenge: | Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts. |
| Approach: | They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmark datasets. |
Generating Fluent Adversarial Examples for Natural Languages (P19-1)
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| Challenge: | Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded. |
| Approach: | They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems. |
| Outcome: | The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI. |
PLATO-Ad: A Unified Advertisement Text Generation Framework with Multi-Task Prompt Learning (2022.emnlp-industry)
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Zeyang Lei, Chao Zhang, Xinchao Xu, Wenquan Wu, Zheng-yu Niu, Hua Wu, Haifeng Wang, Yi Yang, Shuanglong Li
| Challenge: | Online advertisement text generation models have achieved remarkable success in generating high-quality text ads, but some challenges remain, such as low-resource scenarios and training efficiency for multiple ad tasks. |
| Approach: | They propose a unified text ad generation framework with multi-task prompt learning to tackle low-resource ade generation problem and a multi-step prompt learning mechanism to efficiently solve multiple aed generation tasks. |
| Outcome: | The proposed framework outperforms the state-of-the-art on offline and online metrics. |
EventRAG: Enhancing LLM Generation with Event Knowledge Graphs (2025.acl-long)
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Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen, Qiang Zhang
| Challenge: | Existing approaches to text generation often neglect event structures that shape real-world narratives. |
| Approach: | They propose a framework that integrates structured event semantics with iterative retrieval and inference to enhance text generation. |
| Outcome: | Experiments on UltraDomain and MultiHopRAG show that the proposed framework outperforms baseline RAG systems in generation effectiveness, logical consistency, and multi-hop reasoning accuracy. |
ASTRO: Automatic Strategy Optimization For Non-Cooperative Dialogues (2025.findings-acl)
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| Challenge: | Existing methods for optimizing dialogues require substantial human effort for strategy optimization. |
| Approach: | They propose a fully automated solution that leverages large language models’ self-envolving capabilities to optimize dialogue strategies. |
| Outcome: | The proposed solution significantly improves on baseline models across non-cooperative dialogue tasks, highlighting the potential for autonomously developing such agents without human intervention. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)
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| Challenge: | Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models. |
| Approach: | They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters. |
| Outcome: | The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation. |
Double Graph Based Reasoning for Document-level Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction fail to recognize relations between entities across sentences. |
| Approach: | They propose a method to recognize relations for long paragraphs by a Graph Aggregation-and-Inference Network (GAIN) they propose to use a heterogeneous mention-level graph and an entity-level EG graph to analyze the relationships. |
| Outcome: | The proposed method achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. |
Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases (2025.findings-acl)
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Yongjia Lei, Haoyu Han, Ryan A. Rossi, Franck Dernoncourt, Nedim Lipka, Mahantesh M Halappanavar, Jiliang Tang, Yu Wang
| Challenge: | Existing methods for textual and structural retrieval ignore mutual reinforcement and only use structural retrievals for text-rich Graph Knowledge Bases (TG-KBs). |
| Approach: | They propose a Mixture of Structural-and-Textual Retrieval to retrieve textual and structural knowledge via a Planning-Reasoning-Organizing framework. |
| Outcome: | Experiments show that the proposed framework performs better than existing methods in analyzing TG-KBs and integrating structural trajectories for candidate reranking. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
What Does Infect Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs (2026.eacl-long)
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| Challenge: | S-MedQA is an English question-answering dataset designed for benchmarking large language models in fine-grained clinical specialties. |
| Approach: | They propose to use an English medical question-answering dataset to benchmark large language models in clinical specialties. |
| Outcome: | The proposed dataset is designed to benchmark large language models in medical specialties. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
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| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
DARL: Encouraging Diverse Answers for General Reasoning without Verifiers (2026.findings-acl)
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| Challenge: | Recent efforts such as RLPR have extended RLVR to general domains, enabling training on broader datasets and achieving improvements over RL PR. |
| Approach: | They propose a framework that encourages the generation of diverse answers within a controlled deviation range from the reference while preserving alignment with it. |
| Outcome: | Extensive experiments on 13 benchmarks show that DARL surpasses RLPR in both reasoning accuracy and output diversity. |
Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (2021.acl-long)
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| Challenge: | Existing methods to detect false claims ignore the characteristics of FC-articles . claims are often quoted to describe checked events, providing lexical information . sentence templates to introduce or debunk claims are common across articles, providing pattern information. |
| Approach: | They propose a model to rerank FC-articles using key sentences and pattern information. |
| Outcome: | The proposed model outperforms existing methods on two real-world datasets showing that key sentences can be used to predict if an article fact-checks the given claim. |
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning. |
| Approach: | They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds. |
| Outcome: | The proposed task solves the problems in an aspect-based sentiment analysis task. |
Evaluating Generative Language Models in Information Extraction as Subjective Question Correction (2024.lrec-main)
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| Challenge: | Modern large language models (LLMs) perform poorly in elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation methods. |
| Approach: | They propose a method to evaluate large language models by incorporating a human annotation schema. |
| Outcome: | The proposed evaluation method improves matching between model outputs and golden labels. |
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge. |
| Approach: | They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts. |
| Outcome: | The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts. |
Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
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| Challenge: | a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations . |
| Approach: | They propose to identify all event causal relations in a document, both within a sentence and across sentences. |
| Outcome: | The proposed model improves the performance of causal relation identification . it shows that the model can be used to identify cross-sentence causal relations . |
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models (2026.findings-acl)
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Hoang Phan, Xianjun Yang, Yuanshun Yao, Jingyu Zhang, Shengjie Bi, Xiaocheng Tang, Madian Khabsa, Lijuan Liu, Deren Lei
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning . however, the recipe introduces a significant risk of capability regression, where models forget foundational skills after prolonged training without employing regularization strategies. |
| Approach: | They propose a replay strategy with dynamic objective reweighting for general knowledge preservation using short-horizon signals of convergence and instability. |
| Outcome: | The proposed method preserves general capabilities and improves reasoning . it can be applied to existing RLVR pipelines without training additional models or tuning . |
Provably Confidential Language Modelling (2022.naacl-main)
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| Challenge: | Existing methods to train language models without memorizing sensitive data are mismatched and can be difficult to screen and filter. |
| Approach: | They propose a method to train language generation models while protecting the confidential segments of training data. |
| Outcome: | The proposed method prevents unintended memorization by randomizing parts of the training process while protecting strong confidentiality. |
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)
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| Challenge: | Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient. |
| Approach: | They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve. |
| Outcome: | The proposed approach improves on three SLT datasets conducted under the gloss-free setting. |
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (D18-1)
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| Challenge: | Existing dialog datasets rely on human labeling, which is expensive, limited in size, and in low coverage. |
| Approach: | They propose a framework to automatically cluster dialogue intents and slots . they collect context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. |
| Outcome: | The proposed framework can promote human labeling cost to a great extent and achieve good intent clustering accuracy (84.1%) it also provides reasonable and instructive slot labeling results. |
TableBank: Table Benchmark for Image-based Table Detection and Recognition (2020.lrec-1)
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| Challenge: | Existing techniques for table detection and recognition are limited to document types and layouts. |
| Approach: | They propose to build a table detection and recognition dataset with weak supervision from Word and Latex documents on the internet. |
| Outcome: | The proposed dataset contains 417K high quality labeled tables and is publicly available. |
Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)
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| Challenge: | Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult. |
| Approach: | They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB. |
| Outcome: | The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP. |
PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance (2022.emnlp-main)
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| Challenge: | Existing studies on financial question answering systems focus on passively responding to user queries. |
| Approach: | They propose a new dataset to facilitate conversational question answering over hybrid contexts in finance . they propose PACIFIC to combine clarification question generation and CQA . |
| Outcome: | The proposed method performs multi-task learning over all sub-tasks in PACIFIC . it incorporates a simple ensemble strategy to alleviate error propagation issue . |
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)
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| Challenge: | Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare. |
| Approach: | They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss. |
| Outcome: | Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption. |
Boosting Logical Fallacy Reasoning in LLMs via Logical Structure Tree (2024.emnlp-main)
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| Challenge: | Logical fallacy is the use of invalid or flawed reasoning in the construction of a statement. |
| Approach: | They propose to build a logical structure tree to represent hierarchical logic flow among relation connectives and their arguments in a statement. |
| Outcome: | The proposed model significantly improves accuracy and recall for fallacy detection and fallacy classification. |
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge (P19-1)
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| Challenge: | Existing methods for topic-to-essay generation are insufficient for generating novel, diverse, and topic-consistent paragraph-level text with a set of topics. |
| Approach: | They propose to integrate commonsense from external knowledge base into the generator through dynamic memory mechanism and adversarial training to further improve topic-consistency. |
| Outcome: | The proposed task is more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation. |
Simple Recurrent Units for Highly Parallelizable Recurrence (D18-1)
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| Challenge: | recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. |
| Approach: | They propose a simple recurrent unit that provides expressive recurrence and allows highly parallel implementation. |
| Outcome: | The proposed model achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and delivers stronger results than LS and convolutional models. |
A Group Fairness Lens for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods focusing on a few groups lack a comprehensive categorical perspective to evaluate LLMs’ potential biases and unfairness. |
| Approach: | They propose to evaluate LLM biases from a group fairness lens using a hierarchical schema characterizing diverse social groups. |
| Outcome: | The proposed method mitigates biases in LLMs from a group fairness lens and encapsulates target-attribute combinations across multiple dimensions. |
AesX: Enhance Your Images with Stunning Aesthetic Beauty (2026.acl-industry)
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| Challenge: | Existing models do not analyze human preferences at a finer granularity, which leads to quality issues. |
| Approach: | They propose a set of preference indicators across two major dimensions, text-image consistency and aesthetic quality, and a generative framework to steer the model toward a generation path that more closely aligns with human aesthetic sensibilities. |
| Outcome: | The proposed model improves target recognition accuracy and overall visual aesthetic presentation by focusing on human preferences. |
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents (2024.findings-acl)
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| Challenge: | Existing methods for addressing ambiguities in conversational search systems are one-size-fits-all and struggle to achieve effective domain transferability. |
| Approach: | They propose a method to provide search engines with strategies regarding when to ask clarification questions in a post-hoc manner. |
| Outcome: | The proposed method improves search performance 10% on four unseen domains. |
Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation (2026.acl-long)
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| Challenge: | Current evaluations of large language models (LLMs) are limited in capturing key challenges of clinical diagnostic scenarios. |
| Approach: | They propose a dynamic benchmark for medical diagnostics that provides a stress test of diagnostic robustness. |
| Outcome: | The proposed model provides a stress test of diagnostic robustness and veracity, helpfulness and consistency. |
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)
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Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang
| Challenge: | Existing tools for detecting safety issues in LLMs are expensive and inefficient. |
| Approach: | They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions. |
| Outcome: | The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs. |
PodAgent: A Comprehensive Framework for Podcast Generation (2025.findings-acl)
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| Challenge: | Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. |
| Approach: | They propose a framework for creating podcast-like audio programs that generates informative topic-discussion content by designing a multi-agent collaboration system, builds a voice pool and uses LLM-enhanced speech synthesis to generate expressive conversational speech. |
| Outcome: | The proposed framework surpasses direct GPT-4 generation in topic-discussion dialogue content, and produces more expressive conversational speech. |
R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms (2026.acl-industry)
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| Challenge: | Existing approaches to query–document relevance assessment are limited . ambiguous user intent and asymmetric relevance are challenges for RAG platforms . |
| Approach: | They propose a decomposed reasoning model for relevance assessment that decomposes query intent into intent inference and evidence grounding. |
| Outcome: | The proposed model outperforms strong baselines on offline benchmarks and achieves significant gains in large-scale online A/B testing. |
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
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| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. |
A Practical Examination of AI-Generated Text Detectors for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing methods to detect large language models are prone to misuse, such as generating fake news articles, facilitating academic plagiarism or spamming. |
| Approach: | They evaluate several popular detectors to evaluate their effectiveness against a range of domains, datasets, and models. |
| Outcome: | The proposed methods perform poorly in certain settings, with TPR@.01 as low as 0%. |
Inferring symmetry in natural language (2020.findings-emnlp)
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| Challenge: | Empirical work on predicate symmetry has taken two main approaches: feature-based approach and context-based one denies the existence of absolute symmetry. |
| Approach: | They propose a methodological framework for inferring symmetry of verb predicates in natural language. |
| Outcome: | The proposed framework is based on a dataset of 400 naturalistic verbs spanning the spectrum of symmetry-asymmetry. |
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis (2023.findings-acl)
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Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong Guo, Xiaoli Wang, Lei Yang, Di Niu
| Challenge: | Existing methods for multimodal sentiment analysis focus on general knowledge, which is inadequate to identify specific sentiments across modalities. |
| Approach: | They propose a method where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture. |
| Outcome: | The proposed method outperforms all prior methods on three popular benchmarks on multimodal sentiment analysis metrics. |
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (2023.emnlp-main)
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| Challenge: | Existing evaluation benchmarks focus on pairwise matching, ignoring robustness . current models exhibit frustrating degradation, with a maximum drop of 23.43 F1 score . |
| Approach: | They propose a benchmark that simulates the evaluation of open information extraction models in the real world . they perform experiments on typical models published in the last decade and a representative large language model . |
| Outcome: | The proposed model is rated robust on a knowledge-invariant clique with different syntactic and expressive forms. |
Hence, Socrates is mortal: A Benchmark for Natural Language Syllogistic Reasoning (2023.findings-acl)
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Yongkang Wu, Meng Han, Yutao Zhu, Lei Li, Xinyu Zhang, Ruofei Lai, Xiaoguang Li, Yuanhang Ren, Zhicheng Dou, Zhao Cao
| Challenge: | SylloBase is a benchmark for syllogistic reasoning, a critical capability widely required in natural language understanding tasks, such as text entailment and question answering. |
| Approach: | They propose to use a benchmark to learn syllogistic reasoning on a set of templates and to use them to generate and understand slogisms. |
| Outcome: | The proposed benchmark covers a complete taxonomy of syllogism reasoning patterns, and contains both automatically and manually constructed samples. |
Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training (2026.acl-long)
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| Challenge: | Existing methods for reweighting data mixtures rely on manual designation with certain heuristics based on intuition or empirical results. |
| Approach: | They propose a model-based framework that learns to re-weight domains by reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment. |
| Outcome: | The proposed framework outperforms baselines in achieving balanced performance across source and target fields and domain spaces without retraining. |
Can We Edit Factual Knowledge by In-Context Learning? (2023.emnlp-main)
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| Challenge: | In-context knowledge editing (IKE) is a new paradigm for NLP research that can be applied to large language models with tens or hundreds of parameters. |
| Approach: | They propose to use in-context knowledge editing (IKE) without gradient updating to edit factual knowledge without a gradient update. |
| Outcome: | The proposed method achieves a competitive success rate compared to gradient-based methods on GPT-J but with fewer side effects. |
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention (D19-1)
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| Challenge: | Existing approaches to detect suicidal ideation on social media are limited to a small group of people. |
| Approach: | They propose to use tree holes to embed words into microblogs to strengthen the sensibility of suicide-related lexicons and to use a two-layered attention mechanism to grasp intermittently changing points from individual's open blog streams. |
| Outcome: | The proposed approach can achieve over 91% accuracy with the use of suicide-oriented word embeddings and attention on a large-scale well-labelled suicide data set. |
BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook (2026.acl-long)
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Hao Gu, Lujun Li, Hao Wang, Lei Wang, Zheyu Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Sirui Han, Yike Guo
| Challenge: | Recent sparsity-aware binarization approaches can achieve sub-1-bit compression, but they face performance degradation, mask-management overhead, and limited hardware compatibility. |
| Approach: | They propose a binary quantization framework that leverages binary pattern clustering and weight transformation to overcome performance degradation and mask-management overhead. |
| Outcome: | The proposed framework achieves state-of-the-art compression (1.11–0.7 bits) it maintains high performance with only a 3.1% accuracy drop in zero-shot benchmarks while delivering a 1.6 speedup over FP16. |
Collaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing multi-modal knowledge graphs lack modality-specific information and are limited in their ability to capture nuanced semantic interplay between modalities. |
| Approach: | They propose a multi-modal knowledge graph completion method which integrates both paradigms . they use a fine-grained Entity Representation Factorization module and a Robust Relation-aware Modality Fusion module to obtain robust representations for three independent modalities and one fused modality. |
| Outcome: | The proposed method achieves coexistence and collaboration of fused and independent modality representations while maintaining modality-specific information. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
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| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)
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Yuanzhen Xie, Xinzhou Jin, Tao Xie, Matrixmxlin Matrixmxlin, Liang Chen, Chenyun Yu, Cheng Lei, Chengxiang Zhuo, Bo Hu, Zang Li
| Challenge: | In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. |
| Approach: | They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition. |
| Outcome: | The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches. |
SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View (2025.acl-long)
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| Challenge: | despite the potential of large language models, it is difficult to fully count on them in real-world scenarios. |
| Approach: | They propose to examine how LLMs perform during the comprehension process from a cognitive perspective. |
| Outcome: | The proposed model analyzes how LLMs perform during the comprehension process from a cognitive perspective. |
On Tree-Based Neural Sentence Modeling (D18-1)
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| Challenge: | Existing tree-based sentence modeling approaches adopt syntactic parsing trees as the explicit structure prior. |
| Approach: | They replace parsing trees with trivial trees to study their effectiveness . they found that tree-based sentence modeling gives better results when crucial words are closer to the final representation . |
| Outcome: | The proposed tree-based sentences have shown better results on many downstream tasks. |
ERRV: Eliciting Efficient Reasoning through Reasoning Vectors for Policy Optimization in Large Language Models (2026.findings-acl)
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| Challenge: | Existing efforts to improve reasoning efficiency of large language models focus on modifying the reinforcement learning reward, such as adding length penalties. |
| Approach: | They propose a training framework that elicits efficient reasoning through reasoning vectors and a framework that allows the model to generate high-quality responses during reinforcement learning. |
| Outcome: | The proposed framework reduces reasoning length by 30% while maintaining stability, while retaining high accuracy. |
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models (2026.findings-acl)
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Kailin Jiang, Ning Jiang, Yuntao Du, Yuchen Ren, Yuchen Li, Yifan Gao, Jinhe Bi, Yunpu Ma, Bin Li, Lei Liu, Qing Li
| Challenge: | Existing benchmarks for Large Multimodal Models (LMMs) are constrained by static representations, inadequately evaluating their ability to understand time-sensitive knowledge. |
| Approach: | They propose a benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types to evaluate temporal awareness along 6 key dimensions and 11 challenging tasks. |
| Outcome: | The proposed benchmark measures temporal awareness along 6 key dimensions and 11 tasks, while most open-source LMMs still lack time understanding ability. |
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)
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| Challenge: | Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features. |
| Approach: | They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end . |
Bayesian Optimization for Controlled Image Editing via LLMs (2025.findings-acl)
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Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, Lei Li
| Challenge: | achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. |
| Approach: | They propose an off-the-shelf approach that integrates Large Language Models with Bayesian Optimization to facilitate precise and user-friendly image editing. |
| Outcome: | The proposed approach outperforms existing methods in editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4. |
Systematic word meta-sense extension (2023.emnlp-main)
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| Challenge: | Many words in the lexicon are polysemous in that the same word form can express multiple distinct yet related senses. |
| Approach: | They propose a task to extend word meaning to denote new semantic domains that bear regular semantic relations with existing senses. |
| Outcome: | The proposed method improves language models' ability to extend word meaning on multiple benchmarks of figurative language understanding. |
Controllable Text Generation with Focused Variation (2020.findings-emnlp)
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Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
| Challenge: | Focused-Variation Network (FVN) is a new model to control language generation. |
| Approach: | They propose a model that learns discrete latent spaces for each attribute inside codebooks and uses them to generate fluent text. |
| Outcome: | The proposed model can generate fluent and mostly coherent text on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations. |
GEM: A General Evaluation Benchmark for Multimodal Tasks (2021.findings-acl)
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Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
| Challenge: | Existing datasets that focus on natural language tasks are not considered as a general evaluation benchmark for multimodal tasks. |
| Approach: | They present a general evaluation benchmark for multimodal tasks, GEM 1 . they compare it with existing multimodal vision-language datasets . |
| Outcome: | The proposed model is compared with existing vision-language datasets focusing on natural language tasks . it is the largest vision-linguistic dataset covering image-language tasks and video-language task at the same time . |
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning (2026.acl-long)
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Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Fei Huang, Ya-Qin Zhang, Yang Liu
| Challenge: | Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited. |
| Approach: | They propose an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT. |
| Outcome: | Experiments on 7B-scale writer models show that Writing-RL improves long-form writing performance over strong SFT baselines. |
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm (2022.findings-naacl)
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| Challenge: | Prompt-based learning inherits the vulnerability from pre-training, where model predictions can be misled by inserting triggers into the text. |
| Approach: | They propose a potential solution to mitigate this vulnerability by injecting triggers into pre-trained language models using only plain text. |
| Outcome: | The proposed learning paradigm inherits the vulnerability from the pre-training stage . it can totally control or severely decrease the performance of prompt-based models . |
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning (2022.emnlp-main)
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| Challenge: | Existing image captioning approaches treat image-caption pairs indistinctly without considering the differences in their learning difficulties. |
| Approach: | They propose a pretrained vision–language model that measures cross-modal similarity and a model that uses cross-module similarity to measure the difficulty of captioning. |
| Outcome: | The proposed model achieves superior performance and competitive convergence speed to baselines without incurring additional training costs. |
Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach (2023.findings-emnlp)
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| Challenge: | Existing studies on LLMs evaluation with exams are lacking in cognitive research on their overall knowledge structure. |
| Approach: | They conduct an evaluation using a human test dataset based on Bloom Taxonomy to reveal the knowledge structures of Large Language Models and gain insights of their cognitive capabilities. |
| Outcome: | The proposed model can pass AP, SAT, and Leetcode exams, but lacks the cognitive power to perform on human exams. |
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting (2026.acl-industry)
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| Challenge: | Recent industrial credit scoring models rely heavily on manually tuned statistical learning methods due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness. |
| Approach: | They propose a framework that reformulates credit scoring as a multi-scale sequential learning problem. |
| Outcome: | FinLangNet improves KS and bad debt rate by 6.3 pp in real world deployments. |
CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval (2026.findings-acl)
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Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Yiwei Ma, Jiayi Ji, Chenyi Lei, Han Li, Xiaoshuai Sun
| Challenge: | Existing approaches to search for images using single-modality are limited by representation space fragmentation. |
| Approach: | They propose a unified representation framework that achieves efficient query-target alignment . they introduce a multi-level Chain-of-Thought prompting strategy that guides MLMs to generate discriminative, semantically compatible captions for target images . |
| Outcome: | The proposed framework achieves efficient query-target alignment through synergistic components. |
Explore the Reasoning Capability of LLMs in the Chess Testbed (2025.naacl-short)
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| Challenge: | a recent study shows that large language models struggle with long-term, complex reasoning tasks. |
| Approach: | They propose to integrate annotated strategy and tactic into large language models to improve reasoning capability. |
| Outcome: | The proposed model performs better than GPT, Claude, and Gemini models . it integrates annotated strategy and tactic into the model . |
Neural Language Modeling for Named Entity Recognition (2020.coling-main)
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| Challenge: | Experimental results show that named entity recognition systems are faster and more flexible for the size of the corpus. |
| Approach: | They propose to use a neural language model as an alternative to the conditional random field layer for named entity recognition. |
| Outcome: | The proposed system has a significant speed advantage with a marginal performance degradation. |
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings (C18-1)
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| Challenge: | Almost every natural language processing task suffers from data sparseness. |
| Approach: | They propose a method which identify loanwords in monolingual corpora by using cross-lingual word embeddings as core feature and a log-linear model which combines several shallow features to predict the final results. |
| Outcome: | The proposed method outperforms baseline models significantly on loanword identification and translation in four languages and eight translation directions. |
Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)
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Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yuping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei Li
| Challenge: | Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation. |
| Approach: | They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation. |
| Outcome: | The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it. |
AdaptThink: Reasoning Models Can Learn When to Think (2025.emnlp-main)
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| Challenge: | Recent advances in large reasoning models have demonstrated remarkable capabilities in tackling complex tasks. |
| Approach: | They propose an algorithm to teach reasoning models to choose the optimal thinking mode based on problem difficulty. |
| Outcome: | The proposed algorithm reduces the average response length and improves accuracy on three math datasets. |
Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)
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| Challenge: | Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. |
| Approach: | They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages. |
| Outcome: | The proposed framework improves on four low-resource agglutinative language tasks. |
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering (2021.naacl-demos)
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| Challenge: | Using a node-based framework, knowledge base question answering systems can grasp structural mappings between questions and KB queries. |
| Approach: | They propose a node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. |
| Outcome: | The proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. |
CentaurTA: A Self-Improving Human-Agents Collaboration Framework for Thematic Analysis (2026.findings-acl)
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| Challenge: | Existing large language model approaches for qualitative analysis are labor-intensive and costly. |
| Approach: | They propose an iterative human–agent framework for scalable thematic analysis that integrates structured human feedback with rubric-based evaluation. |
| Outcome: | The proposed framework improves coding alignment and transparency across multiple datasets, baselines, and LLM families. |
UniRE: A Unified Label Space for Entity Relation Extraction (2021.acl-long)
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| Challenge: | Existing joint entity relation extraction models setup two separate label spaces for the two sub-tasks . |
| Approach: | They propose to eliminate the different treatment on the two sub-tasks’ label spaces by applying a unified classifier to predict each cell’s label. |
| Outcome: | The proposed model achieves competitive accuracy with the best extractor and is faster. |
Automatic Generation of Personalized Comment Based on User Profile (P19-2)
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| Challenge: | Experimental results show that our model can generate natural, human-like and personalized comments. |
| Approach: | They propose a model that takes user profile into account when generating comments on social media and integrates it with a gated memory. |
| Outcome: | The proposed model can generate natural, human-like and personalized comments on social media. |
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment (2024.emnlp-main)
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Lei Li, Zhihui Xie, Mukai Li, Shunian Chen, Peiyi Wang, Liang Chen, Yazheng Yang, Benyou Wang, Lingpeng Kong, Qi Liu
| Challenge: | Large vision-language models (LVLMs) are evolving rapidly and require data with human supervision to achieve better alignment. |
| Approach: | They introduce VLFeedback, the first large-scale vision-language feedback dataset . they train Silkie, an LVLM fine-tuned via direct preference optimization . |
| Outcome: | The proposed model outperforms its base model in helpfulness, visual faithfulness, and safety metrics and exhibits enhanced resilience against red-teaming attacks. |
Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
| Approach: | They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence. |
| Outcome: | The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models. |
Towards a Deep Understanding of Multilingual End-to-End Speech Translation (2023.findings-emnlp)
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| Challenge: | Recent years have witnessed the rapid development of end-to-end speech-totext translation (ST) which has demonstrated remarkable performance and outperformed conventional cascaded systems. |
| Approach: | They employ Singular Value Canonical Correlation Analysis to analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages. |
| Outcome: | The proposed approach outperforms existing cascaded systems in predicting phonetic features and improves translation quality. |
LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)
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| Challenge: | prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios. |
| Approach: | They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework. |
| Outcome: | The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data. |
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
WildReward: Learning Reward Models from In-the-Wild Human Interactions (2026.acl-long)
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| Challenge: | Prior work focused on collecting preference pairs, requiring substantial annotation efforts. |
| Approach: | They propose a pipeline to extract reliable human feedback from in-the-wild interactions . they propose to use WildChat as an interaction source to train the model . |
| Outcome: | The proposed model achieves comparable or even superior performance compared to conventional models with improved calibration and cross-sample consistency. |
GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art (2025.acl-long)
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Yiming Lei, Chenkai Zhang, Zeming Liu, Haitao Leng, ShaoGuo Liu, Tingting Gao, Qingjie Liu, Yunhong Wang
| Challenge: | Existing benchmarks for video comment art are constrained by their limited modalities and insufficient categories, hindering creativity in video-based comment art creation. |
| Approach: | They propose a benchmark that integrates video and text modalities to evaluate MLLMs’ abilities to compose video Comment art. |
| Outcome: | The proposed framework integrates video and text modalities to evaluate MLLMs’ abilities to compose video comment art. |
Temporal Precision Matters: Brain-Tuning Speech Language Models with Millisecond-Resolution Neural Signals (2026.acl-long)
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| Challenge: | Language models have emerged as powerful tools for predicting human brain activity during language comprehension. |
| Approach: | They propose a technique that leverages electrocorticography’s millisecond precision to train speech language models. |
| Outcome: | The proposed technique improves brain alignment over pretrained and distillation models and produces higher gains in higher-order language regions. |
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)
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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou
| Challenge: | Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. |
| Approach: | They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn . |
| Outcome: | The proposed benchmark is very challenging for state-of-the-art models, it is found. |
Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations (2024.findings-emnlp)
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| Challenge: | State-of-the-art language models (LMs) sometimes generate that misalign with world knowledge. |
| Approach: | They propose a method to mitigate hallucinations by restoring the LM's internal fact recall pipeline by a targeted restoration of its internal fact-recall pipeline. |
| Outcome: | The proposed method shows superior performance compared to baselines. |
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making (2021.acl-long)
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| Challenge: | Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. |
| Approach: | They propose a novel EM framework that consists of Heterogeneous Information Fusion and Key Attribute Tree Induction to decouple feature representation from matching decision. |
| Outcome: | The proposed framework outperforms SOTA EM models on 6 public datasets and 3 industrial datasets. |
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification (2022.coling-1)
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| Challenge: | Existing methods for few-shot text classification suffer from overfitting due to the lack of matching between the few amount of samples and complicated models. |
| Approach: | They propose a method to improve model generalization ability to a new task by leveraging a meta-learner via gradient similarity method. |
| Outcome: | The proposed method improves few-shot text classification performance on several benchmarks. |
Simple and Effective Graph-to-Graph Annotation Conversion (2022.coling-1)
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| Challenge: | Existing work on graph-structured annotation conversions has focused on feature-based models which are not easily applicable to new conversions. |
| Approach: | They propose two graph-to-graph conversion approaches which use pseudo data and inherit parameters to guide conversions respectively. |
| Outcome: | The proposed approaches outperform strong baselines with higher conversion score on a graph-structured dataset and other datasets. |
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)
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| Challenge: | Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. |
| Approach: | They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data. |
| Outcome: | The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification. |
Multilingual Blending: Large Language Model Safety Alignment Evaluation with Language Mixture (2025.findings-naacl)
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| Challenge: | a range of representative Large Language Models have exhibited remarkable generalization capabilities across numerous downstream tasks. |
| Approach: | They propose a query-response scheme to evaluate the safety alignment of LLMs . they found that multilingual query-responding significantly amplifies the detriment of malicious queries . |
| Outcome: | The proposed scheme improves the safety alignment of state-of-the-art LLMs under multilingual conditions. |
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents (2025.emnlp-demos)
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Haofei Yu, Keyang Xuan, Fenghai Li, Kunlun Zhu, Zijie Lei, Jiaxun Zhang, Ziheng Qi, Kyle Richardson, Jiaxuan You
| Challenge: | Existing research systems often design and use agentic workflows to perform research tasks such as ideation, scientific coding, review writing, and tree-based search. |
| Approach: | They propose an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer. |
| Outcome: | The proposed framework adapts easily to new tools and supports iterative growth. |
The Role of Deductive and Inductive Reasoning in Large Language Models (2025.acl-long)
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Chengkun Cai, Xu Zhao, Haoliang Liu, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Lei Li
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning tasks, yet their reliability in problem-solving remains debatable. |
| Approach: | They propose a framework that integrates both deductive and inductive reasoning approaches to enhance LLM reasoning by progressively adapting its reasoning pathways based on problem complexity. |
| Outcome: | The proposed framework achieves 70.3% accuracy on AIW, compared to 62.2% for Tree of Thought, while maintaining lower computational costs. |
Distilling Large Embeddings via Hyperspherical Householder Quantization (2026.acl-long)
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| Challenge: | Existing methods for quantizing large embeddings rely on Euclidean quantization, which is poorly aligned with the angular geometry induced by contrastive embeddment training. |
| Approach: | They propose a geometry-aware distillation method that compresses large embeddings into short discrete representations via iterative Householder transformations on the unit hypersphere. |
| Outcome: | The proposed method reduces decoding cost and maintains strong semantic retrieval accuracy. |
MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning (2020.acl-main)
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| Challenge: | Existing methods for generating paragraph descriptions for videos require a coherent paragraph and a higher level of coherence. |
| Approach: | They propose a new method that generates a summarized memory state from video segments and sentence history to help better predict the next sentence. |
| Outcome: | The proposed method generates more coherent and less repetitive paragraph captions while maintaining relevance to the input video events. |
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)
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| Challenge: | Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications. |
| Approach: | They propose a prototype-based emotion transfer framework that can be used in real-world applications. |
| Outcome: | The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation. |
Reduce Human Labor On Evaluating Conversational Information Retrieval System: A Human-Machine Collaboration Approach (2023.emnlp-main)
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| Challenge: | Evaluating conversational information retrieval systems requires a significant amount of human labor for annotation. |
| Approach: | They propose to use human annotation to calibrate evaluation results to eliminate evaluation biases. |
| Outcome: | The proposed method consumes less than 1% of human labor and achieves a consistency rate of 95%-99% with human evaluation results. |
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)
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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng
| Challenge: | Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting. |
| Approach: | They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance. |
| Outcome: | The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity. |
ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)
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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen
| Challenge: | drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers . |
| Approach: | They propose a framework that automates method statement generation by using multi-agent collaboration. |
| Outcome: | The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. |
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are becoming general-purpose APIs, requiring visual knowledge to be understood. |
| Approach: | They propose to evaluate the visual capability of large-scale large-language models through visual commonsense evaluation using a human-annotated dataset. |
| Outcome: | The proposed dataset compares the visual commonsense knowledge of large-scale models with those of unimodal LLMs and visually augmented models. |
MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning (2026.acl-long)
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| Challenge: | Existing defenses against forgery are inadequate for healthcare. |
| Approach: | They propose a large-scale benchmark for pre-hoc, evidence-grounded medical forgery detection using a doctor inspection guideline and gold edit locations. |
| Outcome: | Experiments show that the proposed solution can detect and explain medical scans with high fidelity and accuracy. |
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases (2024.emnlp-main)
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| Challenge: | Program induction (PI) is a promising paradigm for using knowledge bases (KBs) to help large language models answer complex knowledge-intensive questions. |
| Approach: | They propose a plug-and-play framework that enables large language models to induce programs over any low-resourced KB. |
| Outcome: | Experiments show that KB-Plugin outperforms SoTA low-resourced PI methods with 25x smaller backbone LLM on large-scale and domain-specific KBs and even approaches the performance of supervised methods. |
Leveraging Human and Machine Preferences for Zero-shot Detection of AI-Generated Text (2026.findings-acl)
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| Challenge: | Recent advances in large language models have enabled generated texts to closely mimic human writing, posing significant challenges to the detection of AI-generated content. |
| Approach: | They propose a human-machine prediction discrepancy adapter for AI-generated text detection . they use a joint fine-tuning strategy and a discrepany-aware reweighting mechanism . |
| Outcome: | The proposed framework improves the detection performance of five representative models under various evaluation scenarios. |
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)
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| Challenge: | Existing uncertainty sampling methods are time-consuming and can't be executed frequently. |
| Approach: | They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials. |
| Outcome: | The proposed approach outperforms baselines on effectiveness on five datasets. |
Revealing the Barriers of Language Agents in Planning (2025.naacl-long)
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| Challenge: | Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood . |
| Approach: | They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions . |
| Outcome: | The proposed model achieves 15.6% on a real-world planning benchmark. |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)
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| Challenge: | Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. |
| Approach: | They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite . |
| Outcome: | The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query . |
CharPoet: A Chinese Classical Poetry Generation System Based on Token-free LLM (2024.acl-demos)
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| Challenge: | Traditional systems in this field usually accept keywords as user inputs, resulting in limited control over content. |
| Approach: | They propose a Chinese classical poetry generation system based on token-free LLMs that allow unrestricted user instructions to be used. |
| Outcome: | The proposed system outperforms traditional systems including Jiuge and GPT-4 in format accuracy and content quality. |
Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)
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| Challenge: | Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. |
| Approach: | They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills. |
| Outcome: | The proposed system improves few-shot end-task learning in these domains. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Approach: | They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Outcome: | The proposed framework maps incomplete learning to causes using observable training and inference signals. |
Cross-modal Contrastive Learning for Speech Translation (2022.naacl-main)
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| Challenge: | Existing approaches for speech translation focus on using additional data from MT and automatic speech recognition (ASR). |
| Approach: | They propose a cross-modal contrastive learning method for end-to-end speech-totext translation. |
| Outcome: | The proposed method outperforms existing methods on a popular benchmark MuST-C. |
SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration (2024.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have a profound impact on a wide range of applications. |
| Approach: | They propose a framework to mitigate the tool-abuse behavior of Large Language Models and propose SMARTCAL to mitigate this issue. |
| Outcome: | The proposed framework improves the performance of LLMs on three datasets with two mainstream tool-use frameworks and shows an 8.6% increase in QA performance and 21.6 percent lower expected calibration error (ECE) than existing methods. |
Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base (2022.findings-naacl)
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| Challenge: | Existing methods for knowledge base question answering lack causality modeling . previous work fails to model such causalities in their pipeline . |
| Approach: | They propose a causal-enhanced table-filler to overcome sequence-modelling issues . they propose an efficient beam-search algorithm to scale complex queries on large-scale KBs. |
| Outcome: | Experiments on LC-QuAD 1.0 show that the proposed method surpasses state-of-the-arts by a large margin while remaining time and space efficient. |
ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis (2026.acl-long)
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| Challenge: | High-Level Synthesis (HLS) is a hardware design tool that can be used to design hardware from C-like languages, but its widespread adoption is limited by strict coding constraints and design-specific optimizations. |
| Approach: | They propose a multi-agent HLS design framework that leverages specialized LLMs for automated debugging and directive tuning. |
| Outcome: | The proposed framework outperforms Gemini-3-pro in debugging and speedups across various HLS kernels and neural network accelerators. |
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)
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| Challenge: | Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability. |
| Approach: | They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on a benchmark dataset. |
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant (2025.findings-emnlp)
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| Challenge: | Auto-SLURP is a benchmark dataset for evaluating multi-agent frameworks powered by large language models. |
| Approach: | Auto-SLURP is a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks . authors propose it extends original SLURP dataset by relabeling data and integrating simulated servers and external services. |
| Outcome: | The proposed dataset extends the original SLURP dataset for natural language understanding tasks. |
Language Tags Matter for Zero-Shot Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing studies on multilingual machine translation have ignored the importance of LTs. |
| Approach: | They propose to use language tag (LT) strategies to indicate translation directions in MNMT to enhance consistency and alleviate off-target issues in zero-shot directions. |
| Outcome: | The proposed model could translate between unsupervised languages and achieve a +8 BLEU score difference over other LT strategies in translation tasks. |
Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)
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| Challenge: | Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors. |
| Approach: | They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. |
| Outcome: | Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods. |
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
| Approach: | They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions. |
| Outcome: | The proposed model achieves 55.5 exact match scores while human performance is 89.7. |
Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)
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Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, Lei Han
| Challenge: | Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. |
| Approach: | They propose a framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. |
| Outcome: | Experiments show that the framework can generate better software quality compared to state-of-the-art frameworks. |
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension (D19-58)
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| Challenge: | Experimental results show that unified model outperforms other models that treat encoding and matching separately. |
| Approach: | They evaluate a unified model with Transformer layers for machine reading comprehension . they find that the model learns different modeling strategies compared with previous models . |
| Outcome: | The unified model outperforms models with Transformer layers on the machine reading comprehension task. |
LongReward: Improving Long-context Large Language Models with AI Feedback (2025.acl-long)
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Jiajie Zhang, Zhongni Hou, Xin Lv, Shulin Cao, Zhenyu Hou, Yilin Niu, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li
| Challenge: | In recent years, significant advancements have been achieved in the development of long-context large language models (LLMs). |
| Approach: | They propose a method that utilizes an off-the-shelf LLM to provide rewards for long-context model responses from four human-valued dimensions: helpfulness, logicality, faithfulness, and completeness. |
| Outcome: | The proposed method improves models’ long-context performance and enhances their ability to follow short instructions. |
Know the Known and the Unknown: Reasonable Answer Generation with Knowledge-Informed Citations (2026.acl-long)
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Yichi Zhang, Zhuo Chen, Lingbing Guo, Jun Xu, Mengshu Sun, Zhizhen Liu, Lei Liang, Wen Zhang, Huajun Chen
| Challenge: | Existing approaches focus on generating multi-level citations linked to specific references, making it verifiable and trustworthy. |
| Approach: | They propose a new data construction pipeline and a benchmark to improve citation granularity and awareness of unknown information. |
| Outcome: | The proposed model improves on the existing benchmark and data construction pipeline and provides citation granularity and awareness of unknown information. |
RACQC: Advanced Retrieval-Augmented Generation for Chinese Query Correction (2025.findings-emnlp)
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Jinbo Su, Lingzhe Gao, Wei Li, Shihao Liu, Haojie Lei, Xinyi Wang, Yuanzhao Guo, Ke Wang, Daiting Shi, Dawei Yin
| Challenge: | Large language models (LLMs) exhibit remarkable capabilities across many tasks, but face critical challenges in the CSC scenario: (1) poor generalization to rare entities in open-domain searches; and (2) failure to adapt to temporal entity variations due to static parameters, resulting in serious over-correction issues. |
| Approach: | They propose a Chinese Spelling Check system with RAG and multi-task learning that integrates dynamic knowledge retrieval and entity-centric RAG to address rare entities. |
| Outcome: | The proposed system outperforms existing baselines in the CSC task and achieves a maximum improvement of +9.92% on the search scenario benchmark and +3.2% on the general-domain dataset. |
Hierarchical Context-aware Network for Dense Video Event Captioning (2021.acl-long)
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| Challenge: | Existing technologies for dense video event captioning generate fine-grained captions for all events in a long untrimmed video. |
| Approach: | They propose a hierarchical context-aware network for dense video event captioning to capture context from various aspects. |
| Outcome: | The proposed model outperforms the existing model on youcook2 and activitynet . it generates coherent captions for events in a long untrimmed video . |
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)
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| Challenge: | Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years . |
| Approach: | They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges. |
| Outcome: | The proposed model-model cooperation paradigm has been a key focus of recent research . it is a novel paradigm that can be applied to a variety of tasks . |
Making Large Language Models Efficient Dense Retrievers (2026.acl-long)
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| Challenge: | Recent studies have shown that fine-tuning large language models for dense retrieval yields strong performance, but their substantial parameter counts make them computationally inefficient. |
| Approach: | They propose a framework for developing efficient retrievers that performs coarse-to-fine compression through a coarse-grained coarse-tuning strategy. |
| Outcome: | The proposed framework reduces model size and inference cost while preserving performance of full-size models. |
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? (2022.findings-acl)
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| Challenge: | Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer. |
| Approach: | They propose to transfer semantic knowledge from rich-resourced languages to low-resource languages by using multilingual transfer learning. |
| Outcome: | The proposed model outperforms baselines and can handle negative transfer. |
Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization (2024.findings-acl)
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| Challenge: | Low-rank adaptation (LoRA) is an efficient way to fine-tune large language models (LLMs) but its memory overhead restricts it to scale up when the model size increases. |
| Approach: | They propose a low-rank adaptation method which decomposes model weight updates into a pair of low-ranked projection matrices and incorporates a regularizer to improve generalization. |
| Outcome: | The proposed method achieves better performance under few-shot settings and similar performance to the SOTA low-rank adaptation methods. |
EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)
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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks. |
| Approach: | They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation. |
| Outcome: | EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% . |
Large-Scale Multimodal Knowledge Graph about Classical Chinese Poetry: Fine-grained Method and Comprehensive Evaluation (2026.findings-acl)
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| Challenge: | Existing studies on classical Chinese poetry are limited by modality constraints, dataset size, or the level of refinement. |
| Approach: | They propose to construct a large-scale and fine-grained multimodal knowledge graph of classical Chinese poetry using an informative ontology graph and a text-image alignment method. |
| Outcome: | The proposed method collects knowledge about classical Chinese poetry from ontology graphs and performs four tasks that demonstrate its comprehensiveness and high quality. |
MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have limitations in grounding ideas and mitigating confirmation bias during refinement. |
| Approach: | They propose a framework that integrates a Motivational Knowledge Graph with a Q-Driven Socratic Ideator to enhance LLM ideation. |
| Outcome: | The proposed framework enhances LLM ideation by integrating a Motivational Knowledge Graph with a Q-Driven Socratic Ideator. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
Hire a Linguist!: Learning Endangered Languages in LLMs with In-Context Linguistic Descriptions (2024.findings-acl)
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| Challenge: | Existing LLMs rarely perform well in unseen, endangered languages . Existing models such as Llama and GPT-4 lack a rich corpus of training data . |
| Approach: | They propose a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. |
| Outcome: | The proposed approach elevates translation capability from GPT-4’s 0 to 10.5 BLEU for 10 language directions. |
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks (2022.coling-1)
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| Challenge: | Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. |
| Approach: | They compare accuracy vs. model size tradeoffs using quantization and distillation methods . they find that pruning provides greater benefit than quantization . |
| Outcome: | The proposed methods reduce model size and can accelerate inference, but their relative benefit and combinatorial interactions have not been rigorously studied. |
Detoxification for LLM: From Dataset Itself (2026.acl-long)
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| Challenge: | Existing methods for large language models focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. |
| Approach: | They propose to localize and rewrite toxic spans in raw corpora with SoCD, which guides an LLM to localized and preserving semantics while preserving toxicity. |
| Outcome: | The proposed method reduces TP from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20 on three LLMs. |
Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue (2025.findings-naacl)
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| Challenge: | Recent efforts to improve depression-diagnosis-oriented dialogue systems ignore the Stigma preventing users from open conversations about their struggles. |
| Approach: | They propose a method to promote a sense of unobtrusiveness within the dialogue system and assessing depression disorder by probing symptoms. |
| Outcome: | The proposed method improves on baselines including unobtrusiveness evaluation of dialogue content and diagnostic accuracy. |
Instance-Guided Prompt Learning for Few-Shot Text Matching (2022.findings-emnlp)
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| Challenge: | Few-shot text matching is a more practical technique to determine whether two texts are semantically identical. |
| Approach: | They propose a pluggable prompt learning method for few-shot text matching . they use the semantics of instances to regulate the effects of the gate on the prompt tokens . |
| Outcome: | The proposed method outperforms baselines on MRPC and QQP. |
Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale. |
| Approach: | They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure . |
| Outcome: | The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency. |
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)
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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks. |
| Approach: | They propose a benchmark to assess the ability of long-context large language models to handle long-text problems. |
| Outcome: | The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint . |
RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)
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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale. |
| Approach: | They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers. |
| Outcome: | The proposed model is based on human-verified QA pairs and contains 15K questions. |
TWT: Table with Written Text for Controlled Data-to-Text Generation (2021.findings-emnlp)
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| Challenge: | Existing methods output hallucinated text that is not faithful on TWT. |
| Approach: | They propose to generate text conditioned on the structured data and a prefix by leveraging pre-trained neural models. |
| Outcome: | The proposed approach outperforms state-of-the-art methods under automatic and human evaluation metrics. |
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)
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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang
| Challenge: | Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications. |
| Approach: | They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics . |
| Outcome: | The proposed model performs best on KwaiChat but is not perfect in this situation. |
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching (2023.findings-emnlp)
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| Challenge: | Existing methods to learn informative entity embeddings are insufficient for semi-supervised entity alignment. |
| Approach: | They propose a semi-supervised method which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings. |
| Outcome: | The proposed method is superior to existing methods on benchmark datasets and further analyses. |
BU-NEmo: an Affective Dataset of Gun Violence News (2022.lrec-1)
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Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Tanti Wijaya
| Challenge: | Using a dataset that contains headline and image pairings from 840 news articles, we explore the relationship between image and text influence on human emotional response. |
| Approach: | They propose to use a U.S. gun violence news dataset that contains headline and image pairings from 840 news articles with 15K high-quality crowdsourced annotations on emotional responses. |
| Outcome: | The proposed dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. |
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model (2024.naacl-long)
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| Challenge: | Existing benchmarks fail to evaluate extremely long-context LLMs or analyze their limitations. |
| Approach: | They propose a Synthetic, Scalable, Systematic evaluation suite for LLMs using SQL execution. |
| Outcome: | The proposed evaluation suite is able to scale text length and difficulty across scenarios and provides strong correlations with real-world benchmarks. |
Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. |
| Approach: | They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence. |
| Outcome: | The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage. |
A Neural Network Architecture for Program Understanding Inspired by Human Behaviors (2022.acl-long)
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| Challenge: | Existing studies for understanding programs do not take human behaviors as reference. |
| Approach: | They propose a graph neural network model that takes human behaviors as reference in understanding programs. |
| Outcome: | The proposed model performs better on code summarization and code clone detection tasks. |
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)
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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen
| Challenge: | Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society. |
| Approach: | They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system . |
| Outcome: | The proposed system simulates trending topics under poisoning attacks on social media platforms. |
Controlling Text Edition by Changing Answers of Specific Questions (2021.findings-acl)
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| Challenge: | In many situations, we need to change specific content in a document. |
| Approach: | They propose a task where we take a long text, a question, and a target answer as input and output a minimally modified text so that it fits the target answer. |
| Outcome: | The proposed task is based on the existing dataset WIKIBIO and tests on a test set. |
DCP: Dual-Cue Pruning for Efficient Large Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing pruning methods for large vision language models use visual tokens to prune . existing methods fail to balance efficiency and semantic alignment due to large number of visual token. |
| Approach: | They propose a cross-modal pruning framework that considers textual semantics and visual self-attention to combine them to achieve efficient inference acceleration. |
| Outcome: | The proposed pruning framework can retain only 25% of the visual tokens, with a minimal performance degradation of only 0.063% on LLaVA-1.5-13B. |
Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)
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| Challenge: | Text-based knowledge graph completion methods neglect knowledge contexts in inferring process. |
| Approach: | They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. |
| Outcome: | The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets. |
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving (2022.findings-naacl)
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| Challenge: | Existing work on math word problem solvers replace real numbers with symbolic placeholders to focus on logic reasoning. |
| Approach: | They propose to inject numerical properties into symbolic placeholders with contextualized representation learning schema to solve number representation dilemma. |
| Outcome: | The proposed model can solve MWP problems on English and Chinese benchmarks. |
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback (2026.findings-acl)
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Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen
| Challenge: | Time series anomaly detection (TSAD) has traditionally focused on binary classification and lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. |
| Approach: | They propose a time-series reasoning task that reformulates TSAD from discriminative to reasoning-intensive paradigm. |
| Outcome: | The proposed task reformulates TSAD from discriminative to reasoning-intensive paradigm. |
KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding (2023.findings-acl)
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| Challenge: | Existing benchmarks for deep text understanding have encountered two major limitations . most require human annotation of knowledge, which leads to limited knowledge coverage . |
| Approach: | They propose a benchmark to help readers understand a document with prior knowledge . they use massive knowledge bases to guide annotators and large language models to construct knowledgable questions . |
| Outcome: | The proposed benchmarks have limited knowledge coverage and use choices or spans as answers, which results in narrow answer space. |
Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought reasoning. |
| Approach: | They propose a method to solve complex questions with a tree-of-thought approach using parametric knowledge and retrieved external knowledge to augment CoT reasoning. |
| Outcome: | The proposed approach outperforms SOTA methods on three Complex QA datasets under the open-domain setting. |
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)
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| Challenge: | Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization. |
| Approach: | They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding. |
| Outcome: | The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks. |
MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)
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| Challenge: | Using MTG, we train and evaluate multilingual text generation models using human-annotated data. |
| Approach: | They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages. |
| Outcome: | The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios. |
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (N19-1)
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| Challenge: | Existing work on question-answering has limited training examples for RRC . question-announced questions are a key component of online commerce . |
| Approach: | They propose to turn customer reviews into a large source of knowledge that can be exploited to answer user questions. |
| Outcome: | The proposed approach improves review reading comprehension on popular language model BERT . it also improves aspect extraction and aspect sentiment classification tasks . |
Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for reinforcement learning (RL) require a large sample size to be implemented. |
| Approach: | They propose a memory-efficient RL algorithm that maximizes a lower bound of the ELBO-based objective. |
| Outcome: | Experiments show that BGPO outperforms previous RL algorithms for diffusion large language models in math problem solving, code generation, and planning tasks. |
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)
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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu
| Challenge: | RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts. |
| Approach: | They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts. |
| Outcome: | The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness. |
Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)
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| Challenge: | Existing studies rely on roll call data to estimate political preference of legislators. |
| Approach: | They propose to integrate voting behavior and public statements on Twitter to jointly model legislators. |
| Outcome: | The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements . |
Contrastive Aligned Joint Learning for Multilingual Summarization (2021.findings-acl)
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| Challenge: | Existing summarization systems for multilingual text summarizing are limited due to the lack of large-scale data in multiple languages. |
| Approach: | They propose a multilingual summarization system that can understand documents in multiple languages and generate summaries in the corresponding language. |
| Outcome: | The proposed model improves over monolingual models in all languages and transferable to other languages. |
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
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| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
Is Reference Necessary in the Evaluation of NLG Systems? When and Where? (2024.naacl-long)
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| Challenge: | Despite recent advances in reference-free metrics, it has not been well understood when and where they can be used as an alternative to reference-based metrics. |
| Approach: | They propose to use reference-free metrics to evaluate NLG systems . they find they have a higher correlation with human judgment and greater sensitivity to deficiencies in language quality . |
| Outcome: | The proposed metrics exhibit higher correlation with human judgment and greater sensitivity to deficiencies in language quality. |
Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin
| Challenge: | Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage. |
| Approach: | They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
| Outcome: | The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
HyperLoRA: Efficient Cross-task Generalization via Constrained Low-Rank Adapters Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to adapt pre-trained language models (PLMs) to emerging tasks are costly and inefficient. |
| Approach: | They propose a meta-network that generates task-specific weights without any optimization. |
| Outcome: | The proposed approach has flexible generalization ability and superior performance over hypenetworks. |
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021.emnlp-main)
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| Challenge: | Existing studies have focused on continual learning of aspect sentiment classification (ASC) tasks in domain incremental learning (DIL) |
| Approach: | They propose a continual learning method that learns a sequence of tasks incrementally . they propose CLASSIC, which uses a domain incremental learning setting . |
| Outcome: | The proposed model is highly effective in a domain incremental learning setting. |
Efficiently Identifying Watermarked Segments in Mixed-Source Texts (2025.acl-long)
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| Challenge: | Existing methods for watermarking entire documents neglect identifying individual watermark segments within long, mixed-source documents. |
| Approach: | They propose a framework for partial watermark detection that detects whether there is a watermark segment in long text and an adaptive online learning algorithm to pinpoint the precise location of watermark segments. |
| Outcome: | The proposed framework outperforms existing methods and is adaptable to other watermarking techniques. |
Improving Personalized Explanation Generation through Visualization (2022.acl-long)
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| Challenge: | Existing explainable recommendation models generate repetitive sentences for different items or empty sentences with insufficient details. |
| Approach: | They propose a visual-enhanced approach to generate rating scores and text explanations using visualization generation and text–image matching discrimination. |
| Outcome: | The proposed approach improves both the text quality and the diversity and explainability of the generated explanations. |
Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation (2025.acl-long)
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| Challenge: | a recent study has focused on detecting media bias in news articles . a multi-document event relation graph is used to generate a neutralized summary . |
| Approach: | They propose to generate a neutralized summary given multiple articles presenting different ideological views. |
| Outcome: | The proposed method mitigates media bias and improves content preservation. |
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding (2023.acl-long)
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Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, W.k. Chan, Chong-Wah Ngo, Mike Zheng Shou, Nan Duan
| Challenge: | Existing work on video temporal grounding for long videos is limited by existing datasets. |
| Approach: | They propose a query-guided window selection strategy and a coarse-to-fine mechanism to speed up inference for long videos. |
| Outcome: | The proposed framework accelerates inference time by 2x on Ego4D-NLQ and 15x on MAD while keeping SOTA results. |
QaDialMoE: Question-answering Dialogue based Fact Verification with Mixture of Experts (2022.findings-emnlp)
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| Challenge: | Existing research on fact verification focuses on news, tables and Wikipedia passages. |
| Approach: | They propose a question-answering dialogue based fact verification with mixture of experts that exploits questions and evidence effectively in the verification process. |
| Outcome: | The proposed approach outperforms previous approaches on three benchmark datasets and achieves state-of-the-art results. |
Dialogue State Tracking with Incremental Reasoning (2021.tacl-1)
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| Challenge: | Empirical results show that our method outperforms the state-of-the-art methods in terms of joint belief accuracy. |
| Approach: | They propose to track dialogue states gradually with reasoning over dialogue turns using the back-end data. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art methods in terms of joint belief accuracy for a large-scale human–human dialogue dataset. |
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (2023.emnlp-main)
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Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Lee
| Challenge: | Large language models (LLMs) have shown unprecedented performance across various tasks. |
| Approach: | They propose an easy-to-use framework that integrates adapters into LLMs . they evaluate adapters on 14 datasets from two different reasoning tasks . |
| Outcome: | The proposed framework can be used to fine-tune open-access language models with task-specific data and instruction data. |
StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion (2024.acl-long)
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| Challenge: | Existing LM-based VC models require offline conversion from source semantics to acoustic features, limiting their deployment to real-time applications. |
| Approach: | They propose a streaming LM-based model for zero-shot voice conversion that uses a fully causal context-aware LM with a temporal-independent acoustic predictor to facilitate real-time conversion given arbitrary speaker prompts and source speech. |
| Outcome: | The proposed model achieves comparable performance to non-streaming VC systems while maintaining a fully causal context-aware LM with a temporal-independent acoustic predictor. |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
| Approach: | They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
| Outcome: | The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)
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| Challenge: | Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity. |
| Approach: | They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space. |
| Outcome: | The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets. |
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM (2024.findings-emnlp)
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Jielin Qiu, Andrea Madotto, Zhaojiang Lin, Paul Crook, Yifan Xu, Babak Damavandi, Xin Dong, Christos Faloutsos, Lei Li, Seungwhan Moon
| Challenge: | Vision-extended LLMs have made significant strides in VQA, but they still encounter significant difficulties in handling queries involving long-tail entities. |
| Approach: | They propose a benchmark to test models' ability to identify entities and provide detailed, entity-specific knowledge by combining 10 images and 10 knowledge-intensive QA pairs. |
| Outcome: | The proposed model outperforms existing methods on the SnapNTell dataset, achieving a 66.5% improvement in the BELURT score. |
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge. |
| Approach: | They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly. |
| Outcome: | The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection. |
Control Image Captioning Spatially and Temporally (2021.acl-long)
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| Challenge: | Existing methods to generate image captions with user intention are still under exploration. |
| Approach: | They propose a model that connects Contrastive constraints and Attention Guidance in a loop manner and engages explicit spatial and temporal constraints to the generating process. |
| Outcome: | The proposed model improves performance on a trace-controlled image captioning task. |
SAPGraph: Structure-aware Extractive Summarization for Scientific Papers with Heterogeneous Graph (2022.aacl-main)
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Siya Qi, Lei Li, Yiyang Li, Jin Jiang, Dingxin Hu, Yuze Li, Yingqi Zhu, Yanquan Zhou, Marina Litvak, Natalia Vanetik
| Challenge: | Abstractive and extractive methods are used to condense long text into concise summaries while retaining essential information. |
| Approach: | They propose to use paper structure to extract paper summaries from long text . they provide a large-scale dataset of COVID-19-related papers . |
| Outcome: | The proposed framework generates more comprehensive and valuable summaries compared to previous work on COVID-19-related papers. |
Investigating and Extending Homans’ Social Exchange Theory with Large Language Model based Agents (2025.acl-long)
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| Challenge: | Social exchange theory (SET) is widely recognized as a basic framework for understanding human interactions and interactions. |
| Approach: | They propose to use large language models to study Homans’ social exchange theory (SET) by constructing a virtual society composed of three LLM agents and having them engage in a social exchange game to observe their behaviors. |
| Outcome: | The proposed model extends Homans’ SET with LLM-based agents and demonstrates consistency between the agent and human behavior. |
SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)
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| Challenge: | Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets. |
| Approach: | They propose to use generative language models to generate CL data using annotated data. |
| Outcome: | The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark. |
ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation (2024.acl-long)
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| Challenge: | Empirical studies demonstrate that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods. |
| Approach: | They propose an analogical reasoning-based approach that enhances automatic annotation accuracy in the interactive data annotation setting and reduces the need for human corrections. |
| Outcome: | Empirical studies show that Araida reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods. |
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other (2024.findings-naacl)
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| Challenge: | Emergent Large Language Models (LLMs) use extraordinary performance and powerful deduction capacity to discern from traditional language models. |
| Approach: | They propose a method that uses weights to compensate quantization error and learnable singular value incremental (LSI) LSI is a technique that helps weights compensate each other conditioned on activation. |
| Outcome: | The proposed method achieves state-of-the-art performance in diverse quantization settings, no matter in weight-only, weight-activation or extremely low bit scenarios. |
CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation (2026.findings-acl)
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| Challenge: | Existing methods to optimize target-directed molecular generation fail to reconcile conflicting objectives without compromising structural validity. |
| Approach: | They propose a condition-aware discrete diffusion framework that allows for conditional denoising guided by heterogeneous structural and property signals. |
| Outcome: | The proposed framework improves on structure-conditioned, property-conditioned and dual-conditioned benchmarks in binding affinity, drug-likeness, and success rate. |
E2EDev: Benchmarking Large Language Models in End-to-End Software Development Task (2026.acl-long)
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| Challenge: | Existing E2ESD benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols. |
| Approach: | They propose a benchmark to assess whether generated software meets user needs . they use a fine-grained set of user requirements and a fully automated testing pipeline . |
| Outcome: | E2EDev is a benchmark to assess whether generated software meets user needs through mimicking real user interactions. |
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)
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Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
| Challenge: | Existing slot filling models memorize inherent patterns of entities and contexts from training data. |
| Approach: | They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution . |
| Outcome: | The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts. |
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)
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| Challenge: | Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates. |
| Approach: | They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion. |
| Outcome: | The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings. |
Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)
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| Challenge: | Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data. |
| Approach: | They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data. |
| Outcome: | The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy. |
Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech (2026.acl-long)
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| Challenge: | Existing synthesis methods cannot guarantee data quality. |
| Approach: | They propose a hierarchical reward that balances translation quality and latency objectives by combining supervised fine-tuning data with supervised inputs. |
| Outcome: | The proposed model can reuse key-value caches across both modalities and eliminate redundant feature recomputation. |
ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction (2021.naacl-main)
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| Challenge: | Sentiment analysis is a key task in e-commerce to detect fine-to-coarse sentiment polarities. |
| Approach: | They propose to use a large-scale Chinese restaurant review dataset ASAP to investigate the sentiment polarities underlying user reviews. |
| Outcome: | The proposed model outperforms state-of-the-art models on both tasks. |
KS-Lottery: Finding Certified Lottery Tickets for Multilingual Transfer in Large Language Models (2025.naacl-long)
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| Challenge: | Existing studies have shown that a small subset of parameters is highly effective in fine-tuning . prior work shows that there are a few additional parameters corresponding to an intrinsic dimension in a well-trained Large Language Model. |
| Approach: | They propose a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning. |
| Outcome: | The proposed method can find the certified winning tickets in the embedding layer, and fine-tuning on the found parameters is guaranteed to perform as well as full fine- tuning. |
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods do not consider that pre-trained models contain a prominently large amount of information regarding word frequencies, thus biasing prototypical neural networks against learning word entities. |
| Approach: | They propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds and propose 'references' for the model enhancement. |
| Outcome: | The proposed method outperforms the state-of-the-art models on nine benchmark datasets and is comparable to the state of the art. |
Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)
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Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios . |
| Approach: | They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets . |
| Outcome: | The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files. |
The Overlooked Repetitive Lengthening Form in Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies have focused on emphatic and expressive language models with informal styles, such as memes and emojis. |
| Approach: | They propose a two-stage Explainable Instruction Tuning framework that can improve LLMs' performance and explainability for RLF with limited samples. |
| Outcome: | The proposed framework can match zero-shot GPT-4 in performance but not explainability for RLF with limited samples. |
Discovering Dialog Structure Graph for Coherent Dialog Generation (2021.acl-long)
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| Challenge: | Existing studies on dialog structure graphs from open-domain dialogs have limited number of dialog states and can be laborious and costly to annotate manually. |
| Approach: | They propose to use dialog structure graph as a model to discover hierarchical latent dialog states and their transitions from corpus to facilitate dialog management in a RL based dialog system. |
| Outcome: | The proposed model can discover meaningful dialog structure graph and significantly improve multi-turn coherence on two benchmark corpora. |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
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| Challenge: | Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability. |
| Approach: | They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference. |
| Outcome: | The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability. |
Attention Consistency for LLMs Explanation (2025.findings-emnlp)
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| Challenge: | Existing interpretability methods face limitations such as low resolution and high computational cost. |
| Approach: | They propose a multi-layer attention consistency score to estimate the importance of input tokens in large language models. |
| Outcome: | The proposed heuristic achieves a favorable trade-off between interpretability quality and computational efficiency . |
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal. |
| Approach: | They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages. |
| Outcome: | The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark. |
SocialEval: Evaluating Social Intelligence of Large Language Models (2025.acl-long)
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Jinfeng Zhou, Yuxuan Chen, Yihan Shi, Xuanming Zhang, Leqi Lei, Yi Feng, Zexuan Xiong, Miao Yan, Xunzhi Wang, Yaru Cao, Jianing Yin, Shuai Wang, Quanyu Dai, Zhenhua Dong, Hongning Wang, Minlie Huang
| Challenge: | Existing work on LLMs does not address their social intelligence (SI) and their discrepancy with humans. |
| Approach: | They propose a script-based bilingual SI benchmark that integrates outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation by manually crafting narrative scripts. |
| Outcome: | The proposed model is based on a script-based bilingual evaluation paradigm that integrates outcome- and process-oriented evaluation by manually crafting narrative scripts. |
RepEval: Effective Text Evaluation with LLM Representation (2024.emnlp-main)
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Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou
| Challenge: | Traditional metrics for automatic text evaluation are tailored to specific tasks, while LLM-based evaluation metrics are costly. |
| Approach: | They propose a metric that leverages projections of LLM representations for evaluation. |
| Outcome: | The proposed metric exhibits higher correlation with human judgments than previous methods on 14 datasets. |
Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs (2025.naacl-long)
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| Challenge: | Randomly concatenating data points can lead to cross-contamination due to the significant difference in their subject matter. |
| Approach: | They propose a method that randomly concatenates data of varying lengths until reaching the designed maximum length to optimize context length and reduce padding. |
| Outcome: | The proposed method significantly improves performance on GSM8K and HumanEval, and also improves fairness and accuracy by 15%. |
When and How to Augment Your Input: Question Routing Helps Balance the Accuracy and Efficiency of Large Language Models (2025.findings-naacl)
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| Challenge: | augmented generation of knowledge-based long-tail questions can be useful for large language models, but can cause significant latency. |
| Approach: | They propose an adaptive question routing framework that uses a query router to augment input to the right time. |
| Outcome: | The proposed framework surpasses existing approaches in accuracy and efficiency on benchmarks such as AmbigNQ, HotpotQA, MMLU-STEM, and PopQA. |
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals (2022.acl-long)
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| Challenge: | a dialog system posits that users have figured out clear and specific goals . but in many real-world scenarios, users struggle to figure out specific goals by determining all the necessary slots. |
| Approach: | They propose a mixed-type dialog model with a Prompt-based continual learning mechanism . they collect 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains . |
| Outcome: | The proposed model provides user-goal-related knowledge to help figure out clear and specific goals . it can be extended to any specific type by utilizing existing dialog corpora effectively. |
Predicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining (2021.emnlp-main)
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| Challenge: | Natural language relies on a finite lexicon to express an unbounded set of emerging ideas. |
| Approach: | They propose a framework that exploits the cognitive mechanisms of chaining and multimodal knowledge to predict emergent compositional expressions through time. |
| Outcome: | The proposed framework predicts emergent compositions through time using cognitive mechanisms . it is based on modal knowledge and categorizing models of chaining in a syntactically parsed English corpus . |
Planning-Driven Programming: A Large Language Model Programming Workflow (2025.acl-long)
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| Challenge: | Recent research suggests continuous program refinements through visible tests to improve code generation accuracy in large language models (LLMs). |
| Approach: | They propose an LLM programming workflow to improve both initial code generation and subsequent refinements within a structured two-phase workflow. |
| Outcome: | The proposed workflow improves both initial code generation and subsequent refinements within a structured two-phase workflow. |
Neural Open Information Extraction (P18-2)
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| Challenge: | Existing Open IE systems are built on hand-crafted patterns from syntactic parsing, yet they face errors in propagation and compounding at each stage. |
| Approach: | They propose a neural Open IE approach with an encoder-decoder framework . they propose to learn highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open ie system. |
| Outcome: | The proposed approach outperforms baseline methods while maintaining comparable computational efficiency. |
WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? (2025.emnlp-main)
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An-Lan Wang, Jingqun Tang, Lei Liao, Hao Feng, Qi Liu, Xiang Fei, Jinghui Lu, Han Wang, Hao Liu, Yuliang Liu, Xiang Bai, Can Huang
| Challenge: | Existing benchmarks for document understanding in the wild are based on scanned or digital documents . however, these benchmarks fail to capture the challenges posed by documents in the real world . |
| Approach: | They propose a new benchmark that incorporates a diverse set of manually captured document images reflecting real-world conditions. |
| Outcome: | The proposed model is based on a set of manually captured document images reflecting real-world conditions and is compared with digital or scanned documents. |
ThinkQE: Query Expansion via an Evolving Thinking Process (2025.findings-emnlp)
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| Challenge: | LLM-based methods often generate narrowly focused expansions that overlook these desiderata. |
| Approach: | They propose a test-time query expansion framework that promotes exploration and result diversity . ThinkQE encourages deeper and comprehensive semantic exploration and a corpus-interaction strategy that iteratively refines expansions . |
| Outcome: | The proposed framework outperforms prior approaches on diverse web search benchmarks. |
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023.emnlp-main)
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| Challenge: | Existing computational methods for DDI prediction fail to capture interactions for new drugs due to the lack of knowledge. |
| Approach: | They propose a problem setup as zero-shot DDI prediction that deals with the case of new drugs by using textual information from online databases. |
| Outcome: | The proposed method improves on several settings including zero-shot and few-shot DDI prediction and the selected texts are semantically relevant. |
Progressive Planning and Reinforced Reasoning: Large Language Model-Guided Multi-hop Question Answering over Knowledge Graph (2026.findings-acl)
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| Challenge: | Existing approaches to multi-hop question answering lack effective intermediate guidance and policy networks focus on local neighborhood information, making it difficult to anticipate the long-term consequences of decisions. |
| Approach: | They propose a framework that converts decomposed sub-question sequences into stepwise decision guidance and a structure-aware lookahead policy network to enhance the agent's global state awareness and decision foresight in complex environments. |
| Outcome: | The proposed framework surpasses state-of-the-art methods while showing strong generalization. |
CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation (2020.acl-main)
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| Challenge: | Existing methods for generating emotion-controllable response are inadequate due to content consistency and lack of coherence. |
| Approach: | They propose a framework that extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively. |
| Outcome: | The proposed framework outperforms baseline models in coherence, diversity, and relation to emotion factors. |
Dynamic Nonlinear Mixup with Distance-based Sample Selection (2022.coling-1)
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| Challenge: | Existing methods to augment data with mixup are limited by the space of synthetic data and its regularization effect. |
| Approach: | They propose a dynamic nonlinear mixup with distance-based sample selection which generates multiple sample pairs based on the distance between each sample. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple public datasets. |
Design Choices for Extending the Context Length of Visual Language Models (2025.acl-long)
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| Challenge: | Existing open-source Visual Language Models lack systematic exploration into extending their context length, and commercial models often provide limited details. |
| Approach: | They propose to extend Visual Language Models (VLMs) to 128K lengths and open-source the code, data, and models. |
| Outcome: | The proposed model is based on the Qwen-VL series model and is competitive with commercial model GPT-4V. |
CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning (2026.findings-acl)
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| Challenge: | Existing unified structured data question answering methods rely on a set of predefined functions, which restricts their ability to perform complex reasoning beyond these predefined operations. |
| Approach: | They propose a novel adaptive code-driven framework that generates code-based reasoning operations based on a question. |
| Outcome: | The proposed framework improves on multiple structured datasets on real-world scenarios. |
Better Document-Level Machine Translation with Bayes’ Rule (2020.tacl-1)
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| Challenge: | Existing document translation models are based on autoregressive language models, but they are not able to be learned from monolingual documents. |
| Approach: | They propose to use Bayes' rule to create document translation models that can be learned from only parallel sentences and monolingual documents. |
| Outcome: | The proposed model outperforms existing document translation approaches and is based on a novel left-to-right beam-search algorithm. |
DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping (2026.acl-long)
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| Challenge: | Current Large Language Models (LLMs) rely on coarse-grained national labels for pluralistic value alignment. |
| Approach: | They propose a framework for fine-grained pluralistic value alignment using demographic constraints. |
| Outcome: | The proposed framework can identify groups with predictable, high-consensus value preference . it achieves 48.6% accuracy, surpassing open-source LLM DeepSeek-v3.2 . |
CogEmp:A Cognitive Empathy-Oriented Dialogue System for Structured Psychological Counseling (2026.findings-acl)
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| Challenge: | Existing models lack accurate modeling of cognitive empathy, especially the ability to understand users’ emotions and their underlying psychological causes. |
| Approach: | They propose a model tailored for the Chinese cultural context that integrates cognitive empathy into LLMs. |
| Outcome: | The proposed model outperforms existing models in key evaluation metrics, particularly in empathy, comprehensibility, and professionalism. |
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025.coling-main)
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| Challenge: | Existing prompting techniques for Large Multi-Modal Models (LMMs) focus on improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination. |
| Approach: | They propose a prompting scheme that scaffolds coordinates to promote vision-language coordination in Large Multi-Modal Models (LMMs) they overlay a dot matrix within the image as visual information anchors and leverage multi-dimensional coordinates as textual positional references. |
| Outcome: | Experiments on a wide range of vision-language tasks show the superiority of SCAFFOLD prompting over the textual Chain-of-Thought prompting. |
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships (2021.findings-emnlp)
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| Challenge: | Existing knowledge-enhanced pretrained language models focus on entity information and ignore fine-grained relationships between entities. |
| Approach: | They propose to incorporate KG into the language learning process to obtain a KG-enhanced pretrained Language Model. |
| Outcome: | The proposed model improves on several knowledge-driven tasks, such as entity typing and relation classification, compared with the state-of-the-art knowledge-enhanced PLMs. |
Logic-Thinker: Teaching Large Language Models to Think more Logically. (2025.findings-emnlp)
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| Challenge: | Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) LongCoT still faces challenges such as redundancy and logical incoherence. |
| Approach: | They propose a neural-symbolic reasoning framework that generates chains of thought . they propose Logic-Thinker, which transforms symbolic solvers into chains of thoughts . |
| Outcome: | The proposed framework outperforms models fine-tuned with ThinkerCoT on logic reasoning tasks. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Learning to Ask Denotative and Connotative Questions for Knowledge-based VQA (2024.findings-emnlp)
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| Challenge: | Large language models have attracted increasing attention due to their prominent performance on various tasks. |
| Approach: | They propose to let LLMs learn to ask informative questions to collect visual information . they introduce concepts of denotation and connotation to promote image and question understanding . |
| Outcome: | The proposed model can generate high-quality questions and efficiently collect required information without expensive training or annotations. |
Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)
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| Challenge: | Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users. |
| Approach: | They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives. |
| Outcome: | The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives. |
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response. |
| Approach: | They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples. |
| Outcome: | The proposed model outperforms baseline models in producing coherent and empathetic responses. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph (2026.findings-acl)
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| Challenge: | Existing methods for detecting faithfulness hallucinations are coarse or do not capture the models’ internal reasoning processes, making it difficult to learn. |
| Approach: | They propose a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination using Large language models. |
| Outcome: | The proposed method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k. |
R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to augment large language models with external documents are lacking in the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
| Approach: | They propose to integrate R2AG into R2etrieval augmented generation framework by using a R2-Former to capture retrieval information. |
| Outcome: | The proposed framework fills the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases (C18-1)
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| Challenge: | Existing methods for identifying semantic type of entities are incomplete even in large knowledge bases. |
| Approach: | They propose an attributed and predictive entity embedding method which can fully utilize various kinds of information comprehensively. |
| Outcome: | Experiments on two real DBpedia datasets show that the proposed method outperforms 8 state-of-the-art methods with 4.0% improvement in Mi-F1 and 5.2% improvement in Ma-F1. |
Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering (2025.findings-acl)
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Binquan Ji, Haibo Luo, YifeiLu YifeiLu, Lei Hei, Jiaqi Wang, Tingjing Liao, Wang Lingyu, Shichao Wang, Feiliang Ren
| Challenge: | Existing approaches to solve multi-hop question answering challenges require multiple rounds of retrieval and iterative generation. |
| Approach: | They propose a framework that decomposes complex questions into coherent subquestions . it then iteratively refines these subquests through context-aware rewriting to generate effective query formulations. |
| Outcome: | The proposed framework performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. |
Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria (2025.acl-long)
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Yongqi Leng, Renren Jin, Yue Chen, Zhuowen Han, Ling Shi, Jianxiang Peng, Lei Yang, Juesi Xiao, Deyi Xiong
| Challenge: | Existing evaluation methods are inadequate to evaluate large language models (LLMs). |
| Approach: | They propose a fine-grained generative LLM evaluator with instance-level customazable evaluation criteria that can be used to evaluate large language models. |
| Outcome: | The proposed model outperforms existing LLM evaluators and instruction-tuned LLMs on multiple benchmarks and sets new SOTA results. |
Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering (2025.emnlp-main)
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| Challenge: | Existing taxonomy construction methods lack coherence and granularity . Existing approaches rely on manual or narrowly defined schemes . |
| Approach: | They propose a context-aware hierarchical taxonomy generation framework that integrates LLMs with dynamic clustering. |
| Outcome: | The proposed method outperforms existing methods in taxonomy coherence, granularity, and interpretability. |
From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)
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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu
| Challenge: | Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning. |
| Approach: | They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model. |
| Outcome: | The proposed method improves egocentric reasoning abilities on six tasks. |
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs. |
| Approach: | They propose an efficient generative reward modeling framework grounded in model-internal uncertainty. |
| Outcome: | The proposed framework reduces inference cost while improving answer accuracy. |
Towards an On-device Agent for Text Rewriting (2024.findings-naacl)
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Yun Zhu, Yinxiao Liu, Felix Stahlberg, Shankar Kumar, Yu-Hui Chen, Liangchen Luo, Lei Shu, Renjie Liu, Jindong Chen, Lei Meng
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting, however creating a smaller yet potent language model presents two formidable challenges: costly data collection and absence of emergent capabilities. |
| Approach: | They propose a new instruction tuning method to develop a mo-bile text rewriting model that leverages LLM-generated data and heuristic reinforcement learning, eliminating the need for human data collection. |
| Outcome: | The proposed model surpasses the current state-of-the-art LLMs in text rewriting while maintaining a significantly reduced model size using public benchmark EditEval and our new benchmark. |
CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
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| Challenge: | Existing EE methods do not model event characteristics from large unsupervised data. |
| Approach: | They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures. |
| Outcome: | The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks. |
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)
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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation . |
| Approach: | They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input. |
| Outcome: | The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions. |
Empathy in Diversity: Personalized Depression and Anxiety Therapy via Dialogue State Tracking and Patient-Aware Planning (2026.acl-long)
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Xinwei Yang, Junyi Fan, Yuqing Liu, Jiaxuan Wang, Jiashuai Zhang, Hongru Liang, Wenqiang Lei, Yao Song
| Challenge: | Recent efforts have turned to large language models (LLMs) as therapeutic agents for psychological therapy tasks, yet robustness across diverse patients remains underexplored. |
| Approach: | They propose a realistic role-play protocol for evaluating therapeutic dialogue agents and a de-identified, expert-annotated corpus of therapist–patient dialogues. |
| Outcome: | The proposed framework outperforms baselines on therapeutic outcomes and dialogue quality while improving conversational efficiency. |
Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation (2025.emnlp-main)
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| Challenge: | Existing approaches to natural language transformation (NLT) tasks face significant challenges, such as the computational costs of leveraging large pre-trained models and the limited generalization ability of fine-tuned smaller models. |
| Approach: | They propose a framework that combines prompting with fine-tuning to enhance smaller models by integrating In-Context Examples from retrieval. |
| Outcome: | The proposed framework outperforms existing methods across MT and TST tasks. |
COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)
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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback. |
| Approach: | They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences. |
| Outcome: | The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment. |
Editing Conceptual Knowledge for Large Language Models (2024.findings-emnlp)
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Xiaohan Wang, Shengyu Mao, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Existing knowledge editing methods can modify concept-level definitions, but they can distort instantial knowledge in LLMs, leading to poor performance. |
| Approach: | They construct a benchmark dataset ConceptEdit and establish new metrics for evaluation to investigate the editing capability of LLMs. |
| Outcome: | The proposed methods can modify concept definitions but can distort instantial knowledge in LLMs, leading to poor performance. |
Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)
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| Challenge: | Existing research has focused on the earlier stages of emergency response . lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation is limiting current research . |
| Approach: | They propose a first real-world emergency decision-making dataset EDM-Bench . they propose 'rule-enhanced reasoning framework' that integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability. |
| Outcome: | The proposed framework improves decision safety and interpretability by integrating regulatory knowledge with constrained inference mechanisms. |
ClinAlign: Scaling Healthcare Alignment from Clinician Preference (2026.findings-acl)
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Shiwei Lyu, Xidong Wang, Hao Zhu, Lei Liu, Chaohe Zhang, Jian Wang, Jinjie Gu, Benyou Wang, Yue Shen
| Challenge: | Existing methods for aligning open-ended outputs with fine-grained clinician preferences are weakly grounded in professional guidelines. |
| Approach: | They propose a framework to align large language models' outputs with fine-grained clinician preferences . they propose 119 broadly reusable, clinically grounded principles organized by clinical dimensions . |
| Outcome: | The proposed framework outperforms existing models on HealthBench-Hard and Deepseek-R1 and o3. |
CONDUCT: An Expressive Conducting Gesture Dataset for Sound Control (L18-1)
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| Challenge: | Recent studies on music-gesture relationship focus on sound variations and expressiveness of gestures. |
| Approach: | They propose to use a database to create a set of expressive gestures for orchestral conductors . they assume that the gestures convey some meaning shared by most conductor . |
| Outcome: | The proposed database will be used to train a gesture recognition system for live sound control and modulation. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)
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| Challenge: | Existing work on slot filling uses labeled data from source domains to train a model for target domains. |
| Approach: | They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot. |
| Outcome: | The proposed method outperforms state-of-the-art models on multiple datasets and models. |
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025.coling-main)
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Yuchun Fan, Yongyu Mu, YiLin Wang, Lei Huang, Junhao Ruan, Bei Li, Tong Xiao, Shujian Huang, Xiaocheng Feng, Jingbo Zhu
| Challenge: | Large language models (LLMs) have demonstrated significant improvements in reasoning abilities, but these improvements are primarily focused on English, leading to inferior performance in non-English scenarios. |
| Approach: | They propose a multilingual reasoning alignment approach that fine-tunes the layers responsible for multilingual comprehension in one stage. |
| Outcome: | The proposed method fine-tunes 6 of the 9 layers responsible for multilingual comprehension, while reducing training time by 4.1-11.9 compared to the two-stage method. |
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)
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| Challenge: | Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources. |
| Approach: | They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients. |
| Outcome: | The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines. |
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)
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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu
| Challenge: | a recent study shows that large language models have limited generalization in low-resource languages like Chinese. |
| Approach: | They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private . |
| Outcome: | The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language. |
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models (2021.naacl-main)
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| Challenge: | Recent studies reveal a security threat to natural language processing models, called the Backdoor Attack. |
| Approach: | They propose to hack a model by modifying one single word embedding vector without sacrificing accuracy on clean samples. |
| Outcome: | The proposed method is more efficient and stealthier on sentiment analysis and sentence-pair classification tasks. |
Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods (2020.acl-main)
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| Challenge: | Pre-trained language models can be fine-tuned on task-specific datasets, but fine-timing can lead to over- and/or under-estimation problems. |
| Approach: | They propose a method to transfer probability mass from over-estimated regions to under-estimates by truncating and transferring probability mass between over- and under-estimating regions. |
| Outcome: | The proposed method outperforms the fine-tuning approach on a variety of datasets. |
Pun-GAN: Generative Adversarial Network for Pun Generation (D19-1)
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| Challenge: | Existing methods for generating pun sentences with word senses lack large-scale corpus for supervised learning . a pun is a clever and amusing use of a word with two meanings (word senses) |
| Approach: | They propose an adversarial generative network for pun generation with a generator and a discriminator to distinguish between generated pun sentences and real sentences with specific word senses. |
| Outcome: | The proposed network generates sentences that are more ambiguous and diverse in both automatic and human evaluation. |
TESTEVAL: Benchmarking Large Language Models for Test Case Generation (2025.findings-naacl)
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Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, Lei Ma
| Challenge: | Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases. |
| Approach: | They propose a test case generation benchmark that uses large language models to generate unit test cases. |
| Outcome: | The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths. |
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)
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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding
| Challenge: | Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges. |
| Approach: | They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs. |
| Outcome: | The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges . |
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Adaptive Retrieval-Augmented Generation (RAG) is an effective strategy to alleviate hallucination of large language models (LLMs). |
| Approach: | They propose a novel adaptive RAG model that extracts self-aware uncertainty of large language models from their internal states and invokes retrieval accordingly. |
| Outcome: | The proposed model outperforms existing adaptive RAG methods on complex and simple Question Answering datasets. |
Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
| Approach: | They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly . |
| Outcome: | The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages. |
HIPO: A Hierarchical Prompt Optimization Framework with Task Awareness and Fine-Grained Debugging (2026.findings-acl)
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| Challenge: | Existing methods for prompt optimization apply the same prompt across all samples . existing methods ignore variation in sample difficulty . |
| Approach: | They propose a framework that shifts the paradigm from dataset-level to sample-level optimization. |
| Outcome: | The proposed framework outperforms baselines on 27 tasks and reduces API calls, token consumption and overall cost by 1.2 to 80. |
BloomEval: A Bloom’s Cognitive Taxonomy-Based Benchmark for Evaluating LRMs via Cognitive Hierarchy Trace (2026.findings-acl)
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| Challenge: | Existing benchmarks for Large Reasoning Models rely on answer correctness, but fail to assess the structural coherence and cognitive soundness of the reasoning process itself. |
| Approach: | They propose a framework that maps a model's reasoning trajectory onto hierarchical cognitive levels and an annotation pipeline to ensure a scalable yet reliable annotation pipeline. |
| Outcome: | The proposed framework detects hierarchy jumps, breaks, and overthinking errors and enables scalable yet reliable annotation. |
VerIF: Verification Engineering for Reinforcement Learning in Instruction Following (2025.emnlp-main)
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| Challenge: | Best practices for RL in instruction following remain underexplored. |
| Approach: | They propose a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model. |
| Outcome: | The proposed method achieves state-of-the-art performance among models of comparable size and generalizes well to unseen constraints. |
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities. |
| Approach: | They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions. |
| Outcome: | The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset. |
Summary Factual Inconsistency Detection Based on LLMs Enhanced by Universal Information Extraction (2025.findings-acl)
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| Challenge: | Recent studies have shown that Large language models can detect factual inconsistencies in summaries but they lack the efficiency and explainability needed to be effective. |
| Approach: | They propose to decouple LLMs’ information extraction and reasoning capabilities to address key challenges and propose a framework for UIEFID to guide fine-tuned LLM methods in extracting unified structured information from documents and summaries. |
| Outcome: | The proposed framework improves the detection accuracy and reduces redundant reasoning on the AGGREFACT benchmark. |
When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents? (2026.acl-long)
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Yibo Peng, James Song, Lei Li, Xinyu Yang, Mihai Christodorescu, Ravi Mangal, Corina S. Pasareanu, Haizhong Zheng, Beidi Chen
| Challenge: | Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses on functional correctness. |
| Approach: | They propose to attack functionally correct yet vulnerable (FCV) patches by combining multi-turn reasoning with tool invocation and environment interaction. |
| Outcome: | The proposed FCV-Attack achieves an attack success rate of 40.7% on GPT-5 Mini + OpenHands. |
Qsnail: A Questionnaire Dataset for Sequential Question Generation (2024.lrec-main)
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| Challenge: | Questionnaires are a professional research methodology used for qualitative and quantitative analysis of human opinions, preferences, and behaviors. |
| Approach: | They propose a questionnaire-based dataset that consists of 13,168 human-written questionnaires. |
| Outcome: | The proposed dataset contains 13,168 human-written questionnaires gathered from online platforms. |
Developing Prefix-Tuning Models for Hierarchical Text Classification (2022.emnlp-industry)
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| Challenge: | Hierarchical text classification (HTC) is a key task in many industrial applications. Pre-trained Language Models (PLMs) have become dominant for most natural language processing (NLP) tasks. |
| Approach: | They investigate how prefix tuning can improve hierarchical text classification . prefix-tuning model only needs less than 1% of parameters to achieve performance . |
| Outcome: | The proposed model can achieve comparable performance to regular full fine-tuning. |
Gradient-guided Unsupervised Lexically Constrained Text Generation (2020.emnlp-main)
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| Challenge: | Existing methods for lexically constrained generation fail when the search space is too large . a novel method to solve the problem is based on gradient-guided optimization . |
| Approach: | They propose a method to solve lexically-constrained generation as an unsupervised gradient-guided optimization problem. |
| Outcome: | The proposed method achieves state-of-the-art compared to previous methods . it is free of parallel data training, flexible to be used in the inference stage of any pre-trained generation model. |
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
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Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
| Challenge: | Existing information extraction (IE) tasks rely on in-context learning with large language models. |
| Approach: | They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates. |
| Outcome: | The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1. |
LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay (2024.emnlp-main)
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Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, Hao Wang
| Challenge: | Existing studies on LLM agents' social behaviors are lacking . previous studies focused on positive social behaviors, leaving research on negative social behaviors relatively scarce. |
| Approach: | They propose a framework that features a multi-agent system facilitating efficient communication and interaction with LLM agents. |
| Outcome: | The proposed framework is based on Avalon and evaluates on game success and analyzes agents’ social behaviors. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)
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| Challenge: | Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models. |
| Approach: | They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages . |
| Outcome: | The proposed model outperforms existing models in OPUS and is faster than existing models. |
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022.emnlp-demos)
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Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li
| Challenge: | Existing knowledge extraction tools are not complete due to emerging entities and relations in real-world applications. |
| Approach: | They propose an open-source knowledge extraction toolkit DeepKE that supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
| Outcome: | The proposed toolkit supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
GCPG: A General Framework for Controllable Paraphrase Generation (2022.findings-acl)
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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen
| Challenge: | Existing studies highlight a special condition under two indispensable aspects of controllable paraphrase generation (CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness. |
| Approach: | They propose a general controllable paraphrase generation framework that integrates lexical and syntactical conditions into a text sequence and uniformly processes them in an encoder-decoder paradigm. |
| Outcome: | The proposed framework can combine lexical and syntactical conditions and improve paraphrase generation. |
Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)
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| Challenge: | Existing methods for addressing logical queries on knowledge graphs neglect missing edges in KGs . Existing approaches focus on addressing missing edges, thereby neglecting the emergence of new entities . |
| Approach: | They propose a query-aware prompt-fused framework that addresses embedding of emerging entities . they propose to use a symbolic query to gather information relevant to the query . |
| Outcome: | The proposed framework addresses embedding of emerging entities through contextual information aggregation. |
The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Direction (2025.findings-acl)
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| Challenge: | Large language models excel on a variety of reasoning benchmarks, but struggle to generalize to unseen questions due to over-reliance on memorized training examples. |
| Approach: | They propose to identify a set of linear features in the model’s residual stream that govern the balance between genuine reasoning and memory recall. |
| Outcome: | The proposed model can be manipulated to activate the most relevant problem-solving capabilities during answer generation. |
BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) extends the capabilities of large language models (LLMs) by providing access to external knowledge. |
| Approach: | They propose a framework that emulates human interactive reading through annotation and re-reading by integrating a thought bubble module that offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
| Outcome: | The proposed framework offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
DiffCL: Difference-Aware Contrastive Learning for Automatic Answer Grading with Multi-Level Semantic Modeling (2026.findings-acl)
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| Challenge: | Existing approaches to automate answer grading lack semantic understanding and scoring consistency. |
| Approach: | They propose a difference-aware AAG framework that integrates heuristic difference labeling with dual-contrastive learning. |
| Outcome: | The proposed method outperforms cross-entropy-based baselines on SciEntsBank and Beetle datasets. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
Retrieval-Enhanced Adversarial Training for Neural Response Generation (P19-1)
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| Challenge: | Existing approaches to dialogue systems are labor-intensive and difficult to scale up. |
| Approach: | They propose a Retrieval-Enhanced Adversarial Training method for neural response generation that leverages an adversarial training paradigm while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. |
| Outcome: | The proposed method outperforms the vanilla Seq2Seq model and conventional adversarial training approach on a large scale dataset. |
Knowledge Vector of Logical Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Logical reasoning is a central capability in LLMs, but understanding their abilities remains poorly understood. |
| Approach: | They propose to refine the knowledge representations of each reasoning type in LLMs to encourage complementarity . they propose to use complementary loss and subspace constraint loss to enhance complementarities . |
| Outcome: | The proposed framework encourages complementarity between the different types of reasoning in LLMs. |
DeCEMBERT: Learning from Noisy Instructional Videos via Dense Captions and Entropy Minimization (2021.naacl-main)
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| Challenge: | Existing methods to train models on unlabeled web videos are noisy and temporally misaligned . authors propose a method that adds captions and constrained attention loss to improve performance . |
| Approach: | They propose a method that adds captions from video frames as auxiliary text input to provide visual cues for learning better video and language associations. |
| Outcome: | The proposed method outperforms state-of-the-art methods on video-and-language tasks . it adds captions and constrained attention loss to improve model performance . |
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023.findings-emnlp)
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| Challenge: | Existing models for word-level autocompletion (WLAC) only use human typed sequences as prefixes in decoding module. |
| Approach: | They propose a novel iterative nonautoregressive instruct generation model for WLAC task . it uses human typed sequences and iterating decoding with subwords to fully utilize input information. |
| Outcome: | The proposed model is more competent in dealing with low-frequency words, and achieves state-of-the-art results on the WMT22 and benchmark datasets. |
Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts (2025.acl-long)
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| Challenge: | Prior work has shown that a single LLM’s concept representations can be captured as steering vectors (SVs) prior work has demonstrated that SVs extracted from smaller LLMs can effectively control the behavior of larger LLM. |
| Approach: | They propose a linear transformation method to bridge LLM concept representations using simple linear transformations to enable efficient cross-model transfer and behavioral control via SVs. |
| Outcome: | The proposed method bridges concept representations across different LLMs and enables efficient cross-model transfer and behavioral control via SVs. |
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)
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Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua
| Challenge: | Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown. |
| Approach: | They propose a benchmark for evaluating large language models using a well-organized taxonomy. |
| Outcome: | The proposed model is based on a well-organized taxonomy and compares it with other models. |
CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation (2025.findings-naacl)
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| Challenge: | Existing metrics for Simultaneous speech translation (SimulST) are inaccurately measuring latency in unsegmented streaming settings. |
| Approach: | They propose to modify existing metrics to correctly measure computation-aware latency for SimulST systems, addressing limitations present in existing metrics. |
| Outcome: | The proposed model is based on a real-time, lowlatency scenario where the model starts generating the textual translation before the entire audio input is processed. |
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)
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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao
| Challenge: | Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio. |
| Approach: | They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities . |
| Outcome: | The proposed model improves in simple and complex scenarios with AI feedback learning. |
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)
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Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang
| Challenge: | Massive open online courses (MOOCs) are a popular educational platform for advanced research. |
| Approach: | They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. |
| Outcome: | The proposed datasets show that they can facilitate research in MOOCs. |
Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance. |
| Approach: | They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances. |
| Outcome: | The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset. |
Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization (2024.emnlp-industry)
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| Challenge: | Existing approaches to generate concise summarizations require extensive modifications to the architecture. |
| Approach: | They propose a lightweight model that can be finetuned to extract salient keyphrases from the source document to enhance ROUGE F1 and recall. |
| Outcome: | The proposed model can be finetuned to extract salient keyphrases without any LLM customization. |
A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification (2023.acl-short)
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| Challenge: | Large language models have been able to perform well on benchmarks, but they are often not able address real-world challenges. |
| Approach: | They propose to refactor hierarchical tasks into a more indicative long-tail prediction task. |
| Outcome: | The proposed method does not require parameter updates and achieves strong performance across multiple datasets. |
LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech (2026.acl-long)
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| Challenge: | Existing methods for forcing alignment are language-specific and prone to temporal shifts. |
| Approach: | They propose a slot-filling paradigm that uses time indices to predict slot positions. |
| Outcome: | The proposed method reduces accumulated temporal shifts by 69% compared with prior methods. |
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models (2023.acl-long)
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| Challenge: | Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. |
| Approach: | They propose a plan-and-solve (PS) prompting that includes a few manual steps to generate reasoning steps and improves the quality of generated reasoning steps. |
| Outcome: | The proposed strategy outperforms Zero-shot-CoT on ten reasoning problems and has comparable performance to 8-shot CoT prompting on the math reasoning problem. |
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)
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Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu
| Challenge: | With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks. |
| Approach: | They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority. |
Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents (2025.emnlp-main)
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Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren, Hao Xu, Hao Fu, Fangkun Zhao, Caixia Yuan, Xiaojie Wang
| Challenge: | Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. |
| Approach: | They propose a new LLM-based Multi-Agent System benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments. |
| Outcome: | The proposed benchmark provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication. |
Low-Resource Language Expansion and Translation Capacity Enhancement for LLM: A Study on the Uyghur (2025.coling-main)
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Kaiwen Lu, Yating Yang, Fengyi Yang, Rui Dong, Bo Ma, Aihetamujiang Aihemaiti, Abibilla Atawulla, Lei Wang, Xi Zhou
| Challenge: | Extensive experiments have shown that our strategy effectively expands the low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data. |
| Approach: | They propose a direct preference optimization based on translation self-evolution to expand low-resource languages into large language models by using Uyghur as an example. |
| Outcome: | The proposed strategy expands low-resource languages supported by large language models and significantly enhances the model’s translation ability in Uyghur with less parallel data. |
Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation (2026.acl-industry)
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| Challenge: | a new approach to news recommendation grounds each suggestion in a rapidly evolving article corpus while addressing implicit user intents that lack explicit retrievable keywords. |
| Approach: | They propose an intent-driven Semantic ID generation paradigm to address these challenges . they map diverse intents to hierarchical SID prefixes and then fuzzy-match them to current news pool . |
| Outcome: | The proposed model achieves 0% hallucination and 12.4% L1 match on a mainstream Chinese news platform. |
INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback (2023.emnlp-main)
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| Challenge: | Existing methods to evaluate the quality of language generation do not provide explicit explanation of their verdicts. |
| Approach: | They propose a fine-grained explainable evaluation metric for text generation that harnesses human instruction and implicit knowledge of GPT-4 to fine-tune it. |
| Outcome: | The proposed model outperforms all other unsupervised metrics on translation, captioning, data-to-text, and commonsense generation tasks. |
A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
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| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
| Outcome: | The proposed model surpasses existing methods by a large margin and achieves state-of-the-art results on a document-level relation extraction dataset. |
Learning from Diverse Reasoning Paths with Routing and Collaboration (2025.emnlp-main)
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| Challenge: | Recent studies suggest that the reasoning abilities of large language models (LLMs) grows with model size and pre-training data. |
| Approach: | They propose to combine quality filtering, conditional routing, and cooperative peer teaching to transfer knowledge from powerful teacher models to compact and transparent students. |
| Outcome: | Experiments show that QR-Distill is superior to traditional methods. |
Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition (2025.emnlp-main)
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Chenxin An, Zhihui Xie, Xiaonan Li, Ming Zhong, Shansan Gong, Lei Li, Jun Zhang, Jingjing Xu, Lingpeng Kong
| Challenge: | Existing research on long-context scaling in language models has focused on managing lengthy input prompts instead of producing long outputs. |
| Approach: | They propose a sequence-level curriculum learning framework that shifts a model’s focus from interpreting long chain-of-thoughts to generating them. |
| Outcome: | Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, show that the proposed approach surpasses standard fine-tuning by over 10% while maintaining robust performance on understanding tasks. |
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization (D19-1)
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| Challenge: | Abstractive summarization tasks are often based on deep reinforcement learning (RL) but the traditional reward system Rouge-L simply looks for exact n-grams matches between candidates and annotated references, which makes the generated sentences repetitive and incoherent. |
| Approach: | They propose to use distributional semantics to measure matching degrees instead of Rouge-L to generate sentences with n-grams matches. |
| Outcome: | The proposed reward has superiority over the existing reward, despite the incoherence of the generated sentences. |
RAP-ID: Mechanistic Prompt Injection Detection via Impostor Behavior Analysis (2026.findings-acl)
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| Challenge: | Existing defenses rely on externally deployed guardrail models or response inspection . current defenses depend on external guardrails or response inspecting . |
| Approach: | They propose a mechanistic, train-free detection framework that operates exclusively on internal state dynamics during the initial forward pass. |
| Outcome: | The proposed framework achieves competitive performance with significant overall improvements compared to heuristic methods. |
What is More Likely to Happen Next? Video-and-Language Future Event Prediction (2020.emnlp-main)
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| Challenge: | Existing models cannot make multimodal commonsense predictions of future events based on video and dialogue . |
| Approach: | They propose a task to predict which event is more likely to happen in a video clip . they use a dataset with 28,726 future event prediction examples from 10,234 videos . |
| Outcome: | The proposed model provides a good starting point but leaves room for future work. |
Latent Distribution Decouple for Uncertain-Aware Multimodal Multi-label Emotion Recognition (2025.findings-acl)
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Jingwang Huang, Jiang Zhong, Qin Lei, Gaojinpeng Gaojinpeng, Ymyang Ymyang, Sirui Wang, PeiguangLi PeiguangLi, Kaiwen Wei
| Challenge: | Existing studies focus on improving fusion strategies and modeling modality-to-label dependencies, but they overlook the impact of aleatoric uncertainty, which is inherent noise in multimodal data. |
| Approach: | They propose a latent emotional distribution decomposition with uncertainty perception framework to model aleatoric uncertainty in multimodal data. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the CMU-MOSEI and M3ED datasets, highlighting the importance of uncertainty modeling in MMER. |
Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
| Approach: | They propose a capsule network with dynamic routing for linear time Neural Machine Translation . they map the source sentence into a matrix with pre-determined size and apply a deep LSTM network to decode the target sequence from the source representation. |
| Outcome: | The proposed network achieves comparable results with the Transformer system on English-German and English-French tasks. |
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes (2023.acl-long)
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| Challenge: | Existing learned metrics perform unsatisfactory across text generation tasks or require human annotations for training on specific tasks. |
| Approach: | They propose a self-supervised approach to train a model-based metric for text generation evaluation using sentences retrieved from a corpus. |
| Outcome: | The proposed model outperforms all prior unsupervised metrics on four text generation evaluation benchmarks, with an average Kendall improvement of 0.158. |
Pre-training Methods for Neural Machine Translation (2021.acl-tutorials)
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| Challenge: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Approach: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Outcome: | This tutorial explains how to make the most of pre-training for neural machine translation. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult. |
| Approach: | They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses . |
| Outcome: | The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata. |
PolyJoin: Semantic Multi-key Joinable Table Search in Data Lakes (2025.findings-naacl)
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| Challenge: | Existing joinable table search methods focus on single key (unary) joins, where a single column is the join key, but are ineffective when dealing with join keys composed of multiple columns (n-ary joins) Existing methods are inefficient when dealing . with joins composed of n-aries, which are prevalent on web table corpora. |
| Approach: | They propose a joinable table search method that finds multi-key joinable tables on the web, given a query table. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on two real-world web table benchmarks. |
CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing (2025.emnlp-main)
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| Challenge: | Open-source code language models (code LMs) are a growing threat for intellectual property protection. |
| Approach: | They propose a black-box code LM watermarking framework that uses rule-based watermarks and utility-preserving injection method for user-level model tracing. |
| Outcome: | The proposed framework shows that it performs well across multiple state-of-the-art code LMs and is harmless compared to existing baselines. |
LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)
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Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu
| Challenge: | Large language model (LLM) routing assigns each query to the best suitable model from an ensemble. |
| Approach: | They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation . |
| Outcome: | The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing. |
Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach (D19-1)
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| Challenge: | Current machine translation techniques are bottlenecked by adequacy issues . we propose automatic detection of missing and wrong translations . |
| Approach: | They propose automatic detection of adequacy errors in MT hypothesis for MT model evaluation by annotating missing and wrong translations in 15000 Chinese-English translation pairs. |
| Outcome: | The proposed model can detect missing and wrong translations in 15000 Chinese-English translation pairs. |
Word sense extension (2023.acl-long)
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| Challenge: | a long-standing effort in natural language processing has focused on word sense disambiguation, but little has been explored about how word meaning is extended toward new context. |
| Approach: | They propose a framework that partitions a word type into two pseudo-tokens that mark its different senses and infers whether the meaning can be extended to convey the sense denoted by the token. |
| Outcome: | The proposed framework outperforms other models in predicting plausible novel senses for over 7,500 English words. |
SURVEYFORGE : On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing (2025.acl-long)
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| Challenge: | SURVEYFORGE automates survey paper writing, but quality gap between LLM-generated and human-written surveys remains significant. |
| Approach: | They propose a survey tool that automatically generates and refines human-written surveys. |
| Outcome: | Experiments show that SURVEYFORGE outperforms previous work such as AutoSurvey in outline quality and content quality. |
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (P18-2)
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| Challenge: | Recent supervised deep learning models have achieved state-of-the-art performance, but there are two other considerations that are important. |
| Approach: | They propose a supervised aspect extraction model using general-purpose embeddings and domain-specific embeddables. |
| Outcome: | The proposed model outperforms state-of-the-art methods without supervision and achieves very good results. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou
| Challenge: | Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks. |
| Approach: | They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework. |
| Outcome: | The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks. |
ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)
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| Challenge: | Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images. |
| Approach: | They propose a transformer-based Chinese text-to-image synthesizer for high-resolution image generation that incorporates linguistic and relational knowledge facts into the model to ensure better performance without the usage of ultra-large models. |
| Outcome: | The proposed model outperforms existing models in Chinese with linguistic and relational knowledge facts. |
CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models (2026.findings-acl)
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| Challenge: | Existing classification-based methods capture noise and spurious correlations while overlooking the underlying causal mechanisms. |
| Approach: | They propose a hallucination detection framework based on structural causal models that captures static and passive signals from internal states and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise. |
| Outcome: | Experiments on four datasets and three widely used LLMs show that the proposed framework improves AUROC and interpretability. |
Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)
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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin
| Challenge: | Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding. |
| Approach: | They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications. |
| Outcome: | The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. |
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction (2022.emnlp-main)
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| Challenge: | Existing CCE methods treat contracts as plain text, creating a barrier to understanding complex contracts. |
| Approach: | They propose a framework to model implicit relations in legal contracts to improve contract understanding . they propose Term-Definition Relation captures the relation between important terms and their definitions . |
| Outcome: | The proposed framework improves on two CCE tasks in conventional and zero-shot settings. |
TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)
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| Challenge: | Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats . |
| Approach: | They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect . |
| Outcome: | The proposed benchmarks show that video large language models exhibit poor temporal perception ability. |
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)
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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou
| Challenge: | Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. |
| Approach: | They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks. |
| Outcome: | The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude. |
Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)
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| Challenge: | Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection. |
| Approach: | They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM. |
| Outcome: | The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead. |
Augmenting Legal Judgment Prediction with Contrastive Case Relations (2022.coling-1)
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| Challenge: | Existing legal judgment prediction methods only consider one case fact description as input, which may not fully utilize information in the data such as case relations and frequency. |
| Approach: | They propose a new perspective that introduces some contrastive case relations to construct case triples as input and a corresponding judgment prediction framework with case triple modeling. |
| Outcome: | The proposed framework can be used to refine encoding and decoding processes using three customized modules on two public datasets. |
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)
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| Challenge: | Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context. |
| Approach: | They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals. |
| Outcome: | The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness. |
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision (2025.emnlp-industry)
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Yongqi Leng, Yikun Lei, Xikai Liu, Meizhi Zhong, Bojian Xiong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Recent advances in outcome-supervised reinforcement learning (RL) have shown strong performance, but this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback. |
| Approach: | They propose a model that models RAG as a Markov Decision Process (MDP) and introduces an efficient pruning strategy to optimize data expansion. |
| Outcome: | The proposed model outperforms existing methods and achieves an average performance improvement of 6.2% across six datasets. |
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020.emnlp-main)
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| Challenge: | Existing pre-training methods are not effective for machine translation tasks. |
| Approach: | They propose a method to pre-train a universal multilingual neural machine translation model . they use random aligned substitution technique to bring words and phrases with similar meanings closer in the representation space. |
| Outcome: | The proposed approach improves translation quality on low, medium, rich resource languages. |
R3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning (2026.acl-long)
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YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai, Lijun Wu, Wei-Ying Ma, Hao Zhou
| Challenge: | Experimental results show that R3 is a superior alternative to traditional search algorithms for multistep retrosynthesis planning. |
| Approach: | They propose a framework that reformulates multistep retrosynthetic planning as a generative reasoning task. |
| Outcome: | The proposed framework achieves state-of-the-art Top-1 accuracy of 43.7% on retrobench . it leverages Large Language Models to reformulate multistep retrosynthesis as a generative reasoning task. |
GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR (2026.acl-long)
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| Challenge: | Existing parameter-efficient methods for RLVR face limitations . low-rank adaptation methods do not account for the distinct optimization dynamics . |
| Approach: | They propose a low-rank adaptation method tailored for RLVR that exploits the anisotropic structure of RL update subspace and extracts its principal directions via Singular Value Decomposition (SVD). |
| Outcome: | Experiments on large reasoning models show that GeoRA outperforms strong low-rank baselines across RLVR settings while showing stronger generalization and less forgetting on out-of-domain tasks. |
UnrealLLM: Towards Highly Controllable and Interactable 3D Scene Generation by LLM-powered Procedural Content Generation (2025.findings-acl)
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SongTang SongTang, Kaiyong Zhao, Lei Wang, Yuliang Li, Xuebo Liu, Junyi Zou, Qiang Wang, Xiaowen Chu
| Challenge: | UnrealLLM is a novel framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation. |
| Approach: | They propose a novel multi-agent framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation. |
| Outcome: | The proposed framework achieves competitive performance in technical metrics and aesthetic quality, offering unique advantages in generation scale and interactivity. |
PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents (2026.eacl-industry)
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Minjia Wang, Yunfeng Wang, Xiao Ma, Dexin Lv, Qifan Guo, Lynn Zheng, Benliang Wang, Lei Wang, Jiannan Li, Yongwei Xing, Junzhe Xu, Zheng Sun
| Challenge: | Publicly available corpora cover only slivers of human activity, such as email threads, chat logs, purchase histories, sensor traces, and provide large-scale supervision for data-hungry machine-learning pipelines. |
| Approach: | They propose a method for synthesizing realistic digital footprints using large language model agents from a structured user profile. |
| Outcome: | The proposed method generates diverse sequences of user events, producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. |
Typos Correction Training against Misspellings from Text-to-Text Transformers (2024.lrec-main)
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| Challenge: | Existing dense retrieval systems suffer from typoed queries due to mistyping or phonetic typing errors. |
| Approach: | They propose a method that incorporates the spelling correction objective into the DR model and a prompt-based augmentation technique to enhance the alignment of the typoed query and its original query. |
| Outcome: | The proposed model outperforms existing typos-aware training approaches and sophisticated training advanced retrievers. |
SenDetEX: Sentence-Level AI-Generated Text Detection for Human-AI Hybrid Content via Style and Context Fusion (2025.emnlp-main)
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| Challenge: | Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse. |
| Approach: | They propose a framework for sentence-level AI-generated text detection via style and context fusion. |
| Outcome: | The proposed framework outperforms baseline models in detection accuracy while exhibiting transferability and robustness. |
Nature-Inspired Population-Based Evolution of Large Language Models (2026.acl-long)
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| Challenge: | a new framework for population-based evolution of large language models is emerging . a population-driven evolution of LLMs is a key component of evolution, authors say . |
| Approach: | They propose a framework that allows for population-based evolution of large language models . they start with a population of parent LLMs and allow this population to evolve . |
| Outcome: | The proposed framework outperforms existing methods on 12 datasets. |
Tsetlin Machine Embedding: Representing Words Using Logical Expressions (2024.findings-eacl)
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| Challenge: | Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing. |
| Approach: | They propose to embed words in vector space using propositional logic instead of dense vectors . they evaluate embeddings on intrinsic and extrinsic benchmarks and visualize word clusters based on their results . |
| Outcome: | The proposed model outperforms GLoVe on six classification tasks. |
Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming (2026.findings-acl)
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Yiming Lei, Yize Fan, Zeming Liu, Jiaji Dong, Hui Qiu, Haitao Leng, Qingjie Liu, Kehai Chen, Tingting Gao, Yunhong Wang
| Challenge: | Existing Multimodal Large Language Models struggle with dynamic interactions due to the scarcity of high-quality interleaved data. |
| Approach: | They propose a large-scale interleaved live interaction Chinese dataset with human-annotated video responses. |
| Outcome: | The proposed model can be used to evaluate live interactions in Chinese over 1,100 hours and 80,037 dialogue turns. |
Geometric Signatures of Compositionality Across a Language Model’s Lifetime (2025.acl-long)
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| Challenge: | linguistic compositionality allows atoms to locally combine to create global meaning . a rich array of meanings at the level of a phrase may be explained by simple rules of composition. |
| Approach: | They propose to relate the degree of compositionality in a dataset to the intrinsic dimension of its representations under an LM, a measure of feature complexity. |
| Outcome: | The proposed model is based on a geometric view of the compositionality of a dataset and the intrinsic dimension of its representations under an LM. |
Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)
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| Challenge: | Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks. |
| Approach: | They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese. |
| Outcome: | The proposed model improves the few-shot table understanding in Chinese. |
R3 Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context (2023.findings-emnlp)
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| Challenge: | Existing studies have evaluated LLMs under noise-free context but the dilemma for LLM to produce inaccurate results under noisy context has not been fully investigated. |
| Approach: | They propose a new method for CoT reasoning using Chain-of-Thought prompting that interacts with LLMs to perform key sentence extraction, variable declaration and answer prediction. |
| Outcome: | The proposed method outperforms existing CoT prompting methods on five reasoning tasks under noisy context. |
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)
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| Challenge: | Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese. |
| Approach: | They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models. |
| Outcome: | The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show. |
LayoutReader: Pre-training of Text and Layout for Reading Order Detection (2021.emnlp-main)
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| Challenge: | Existing methods for reading order detection are too laborious to annotate large datasets. |
| Approach: | They propose to use a large-scale dataset to annotate reading order information for document images . they use XML metadata to capture the reading order of WORD documents . |
| Outcome: | The proposed model performs almost perfectly in reading order detection and improves both open-source and commercial OCR engines in ordering text lines in their results. |
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking (2025.acl-long)
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Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen
| Challenge: | Existing methods to integrate external knowledge into LLMs focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. |
| Approach: | They propose a new paradigm for structural knowledge prompting to integrate external structural knowledge into LLMs by incorporating structural representations. |
| Outcome: | The proposed benchmark SUBARU enables the evaluation of the generalization capabilities of SKP from four perspectives. |
DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)
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| Challenge: | Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information. |
| Approach: | They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation . |
| Outcome: | The proposed model trains on DocBank accurately recognize layout information for a variety of documents. |
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions (P19-1)
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| Challenge: | Several deep learning models have been proposed for solving math word problems (MWPs) but their approaches to capturing features are not specifically designed for MWP. |
| Approach: | They propose to use a group attention mechanism to extract global features, quantity-related features, quantities-pair features and question-related feature in MWPs. |
| Outcome: | The proposed approach performs significantly better than previous state-of-the-art methods and boosts performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.99% on Math 23K with 5-fold cross-validation and from 69.99% to 76.1% on MAWPS. |
CLeVeR: Multi-modal Contrastive Learning for Vulnerability Code Representation (2025.findings-acl)
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| Challenge: | Existing methods for detecting code capture the overall semantics of the code rather than its intrinsic vulnerability-specific semantics. |
| Approach: | They propose an approach that leverages contrastive learning to generate precise vulnerability code representations under the supervision of vulnerability descriptions. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in vulnerability detection tasks by 11.85% and 13.61%. |
Evaluating Gender Bias of LLMs in Making Morality Judgements (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in a multitude of NLP tasks, but are still not immune to limitations such as gender bias. |
| Approach: | They propose to use a dataset to examine whether LLMs possess gender bias when asked to give moral opinions. |
| Outcome: | The proposed models show that they are biased when asked to give moral opinions. |
Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation (2025.naacl-industry)
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| Challenge: | Text2Sql is a task that translates natural language questions and database schemas into SQL queries. |
| Approach: | They employ pure fine-tuning strategy to reduce redundancy by using only 53% of the baseline prompt length to fine- tune the model. |
| Outcome: | The model outperforms the baseline model by 8.2% and 8.6% in Test-suite accuracy (TS) and exact-set-match accuracy (EM) under the most refined Spider dev set of prompts, the model achieves 73.5% and 75.4%, respectively, approaching state-of-the-art (SOTA) levels. |
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024.emnlp-main)
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| Challenge: | Existing arguments generation methods often overlook connections between opinions . Existing methods struggle with providing compelling proof . |
| Approach: | They propose a two-stage framework for argumentative essay generation with a focus on logical enhancement. |
| Outcome: | The proposed framework generates argumentative essays with better logical validity and persuasiveness than baseline models. |
Diagnosing Moral Reasoning Acquisition in Language Models: Pragmatics and Generalization (2025.findings-emnlp)
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| Challenge: | Prior research has shown that LLMs fail to perform satisfactorily on moral cognizance tasks . |
| Approach: | They propose to use curated datasets to improve LLMs' moral cognizance . they find pragmatic dilemma constrains generalization ability of current learning paradigms . |
| Outcome: | The proposed learning paradigms fail to perform on moral cognizance tasks, the authors show . they show that the pragmatic dilemma is the primary bottleneck for moral reasoning acquisition . |
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model (D19-1)
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| Challenge: | Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages. |
| Approach: | They propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model to make better use of seed alignments to propagate over the entire graphs with KG-based constraints. |
| Outcome: | The proposed method can make better use of seed alignments to propagate over entire graphs with KG-based constraints. |
Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)
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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin
| Challenge: | Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment. |
| Approach: | They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning. |
| Outcome: | The proposed framework outperforms baselines and supports generalization across different model configurations and backbones. |
TacoERE: Cluster-aware Compression for Event Relation Extraction (2024.lrec-main)
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| Challenge: | Existing work on event relation extraction focuses on modeling the entire document . existing methods cannot handle long-range dependencies and information redundancy . |
| Approach: | They propose a compression-then-extraction paradigm for event relation extraction . they propose document clustering for modeling event dependencies and then a cluster summarization method . |
| Outcome: | The proposed method simplifies and highlights important text content of clusters for mitigating redundancy and event distance. |
Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs (2026.findings-eacl)
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| Challenge: | Existing sparsity methods lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. |
| Approach: | They propose a Cognitive-Load-Aware Dynamic Activation framework that synergizes statistical sparsity with semantic adaptability. |
| Outcome: | The proposed framework achieves 20% average speedup with less than 2% accuracy degradation outperforming Griffin and TT. |
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)
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Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin
| Challenge: | Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations. |
| Approach: | They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness. |
| Outcome: | The proposed approach outperforms existing methods while achieving superior editing efficiency. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)
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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss. |
| Approach: | They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant. |
| Outcome: | The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting. |
Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations (2024.acl-long)
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| Challenge: | Discursive Socratic Questioning (DISQ) assesses a model's understanding of discourse relations by requiring systematic accuracy over multiple questions. |
| Approach: | They propose a method that evaluates faithfulness of understanding discourse based on question answering. |
| Outcome: | The proposed method evaluates the faithfulness of understanding discourse based on question answering. |
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios (2024.findings-acl)
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Lei Lin, Jiayi Fu, Pengli Liu, Qingyang Li, Yan Gong, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Di Zhang, Kun Gai
| Challenge: | chain-of-thought (CoT) prompting has been shown to be effective on complex reasoning tasks, but the naive greedy decoding used in CoT prompting causes the repetitiveness and local optimality. |
| Approach: | They propose a generalizable ensemble-optimization method that uses a set of reasoning paths to prompt a language model one more time to determine the optimal answer. |
| Outcome: | The proposed method can be generalized to almost all scenarios where the type of input questions and answer format of reasoning paths may be unknown. |
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering (2026.findings-acl)
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| Challenge: | Existing methods for training patients with cognitive impairment rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across domains and severity levels. |
| Approach: | They propose to use steering vectors from contrastive pairs of instructions and responses to capture domain-specific features and introduce a Stochastic Token Modulation mechanism to regulate the intervention probability. |
| Outcome: | The proposed model outperforms baselines in clinical authenticity and severity controllability while remaining open-source. |
Structural Supervision for Word Alignment and Machine Translation (2022.findings-acl)
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| Challenge: | Existing knowledge on syntactic structure neglects the rich structural information from target tokens and the structural similarity between the source and target sentences. |
| Approach: | They propose to incorporate syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multi-task learning. |
| Outcome: | The proposed method outperforms baselines on four publicly available language pairs and consistently outperformed baselines in alignment accuracy and translation quality. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker (2021.acl-long)
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| Challenge: | Existing methods for document-level event extraction are not effective due to two challenges . existing methods fail to extract events whose arguments spread in multiple sentences . |
| Approach: | They propose a document-level event extraction model with a tracker to capture interdependency among the extracted events. |
| Outcome: | The proposed model outperforms existing models on a large-scale dataset by 2.8 F1 . it extracts multiple correlated events and event arguments that scatter across the document . |
One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)
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Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li
| Challenge: | Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality. |
| Approach: | They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. |
| Outcome: | Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks. |
Beyond Perception: Evaluating Abstract Visual Reasoning through Multi-Stage Task (2025.findings-acl)
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| Challenge: | Existing AVR benchmarks focus on single-step reasoning, emphasizing the end result but neglecting the multi-stage nature of reasoning process. |
| Approach: | They propose a multi-stage AVR benchmark based on RAVEN to assess reasoning across varying levels of complexity. |
| Outcome: | The proposed metric considers the correctness of intermediate steps in addition to the final outcomes. |
Human or LLM as Standardized Patients? A Comparative Study in Medical Education (2026.acl-long)
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| Challenge: | Standardized patients (VSPs) are indispensable for clinical skills training but remain expensive and difficult to scale. |
| Approach: | They propose a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior. |
| Outcome: | The proposed framework more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure. |
Autocorrect in the Process of Translation — Multi-task Learning Improves Dialogue Machine Translation (2021.naacl-industry)
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| Challenge: | Existing neural machine translation models are not able to translate dialogues in real life scenarios. |
| Approach: | They propose a joint learning method to identify omission and typos and utilize context to translate dialogue utterances. |
| Outcome: | The proposed method improves translation quality by 3.2 BLEU over baselines and recovers omitted pronouns by 47.16%. |
NL ⇒ Schedule: Evaluate Multitask Scheduling Capability of Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for scheduling from natural language descriptions rely on experts with limited scheduling skills and domain knowledge. |
| Approach: | They propose a model to generate a feasible schedule from natural language descriptions. |
| Outcome: | The proposed framework achieves more robust performance than six state-of-the-art LLM+solver methods. |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |
LumberChunker: Long-Form Narrative Document Segmentation (2024.findings-emnlp)
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| Challenge: | Modern NLP tasks rely on dense retrieval methods to access up-to-date and relevant contextual information. |
| Approach: | They propose a method that leverages an LLM to dynamically segment documents by iterating on a set of sequential passages to identify the point where the content begins to shift. |
| Outcome: | The proposed method outperforms the most competitive baseline by 7.37% in retrieval performance and integrates into a RAG pipeline. |
BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks (2025.findings-acl)
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| Challenge: | Existing studies focus on forward reasoning based planning, but this paradigm doesn't work well for complex tasks. |
| Approach: | They propose to decompose a task into easily executed steps by planning and use a backward reasoning based agent to make the planning starting from the terminal state. |
| Outcome: | The proposed model outperforms existing methods and the proposed modules in a virtual environment that simulates complex tasks based on real-world scenarios. |
UCL-Bench: A Chinese User-Centric Legal Benchmark for Large Language Models (2025.findings-naacl)
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Ruoli Gan, Duanyu Feng, Chen Zhang, Zhihang Lin, Haochen Jia, Hao Wang, Zhenyang Cai, Lei Cui, Qianqian Xie, Jimin Huang, Benyou Wang
| Challenge: | Existing legal benchmarks focusing on knowledge and logic evaluate LLMs on various tasks in legal domain, but few have explored the practical application of LLM by actual users. |
| Approach: | They propose a Chinese user-centric legal benchmark that aims to assess the practical application of LLMs by real users. |
| Outcome: | The proposed model outperforms existing models on various tasks in legal domain but does not outperfect ChatGPT. |
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach (2020.findings-emnlp)
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| Challenge: | Generating natural language under complex constraints is a principled formulation towards controllable text generation. |
| Approach: | They propose a method to specify combinatorial constraints for sentence generation . they use a tree search algorithm embedded into the proposal process of the Markov Chain Monte Carlo . |
| Outcome: | The proposed method achieves consistent and significant improvement on multiple language generation tasks. |
Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment (2022.lrec-1)
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| Challenge: | Existing models for fake news classification are difficult to explain and quality-assure . however, they are black-box-based and lack a clear explanation of their decisions. |
| Approach: | They propose an interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) they use conjunctive clauses to capture lexical and semantic properties of both true and fake news text and use clause ensembles to calculate the credibility of fake news. |
| Outcome: | The proposed framework outperforms baseline models on PolitiFact and GossipCop datasets in terms of accuracy and provides higher F1-score than BERT and XLNet, but lower accuracy. |
Harnessing Large Language Models for Disaster Management: A Survey (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including their emerging role in mitigating threats to human life, infrastructure, and the environment during natural disasters. |
| Approach: | They propose a taxonomy that categorizes existing LLMs based on disaster phases and application scenarios to provide valuable insights for the research community and practitioners . |
| Outcome: | The proposed taxonomy categorizes existing LLMs based on disaster phases and application scenarios. |
Interactive Classification by Asking Informative Questions (2020.acl-main)
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| Challenge: | Existing methods for intent classification rely on a single user input and do not interact with the user to reduce ambiguity and improve the final prediction. |
| Approach: | They propose a limited form of interaction to natural language intent classification . they add binary or multi-choice questions to the system to ask missing information . |
| Outcome: | The proposed method can be bootstrapped without interaction data and is scalable to two domains. |
ADELIE: Aligning Large Language Models on Information Extraction (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) struggle to follow complex instructions of IE tasks due to not being aligned with humans. |
| Approach: | They propose an aligned large language moDEL that effectively solves various IE tasks including closed IE, open IE and on-demand IE. |
| Outcome: | The proposed model achieves state-of-the-art (SoTA) performance among open-source models. |
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)
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| Challenge: | Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue. |
| Approach: | They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items . |
| Outcome: | The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set. |
How Vocabulary Sharing Facilitates Multilingualism in LLaMA? (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) show strong performance on English tasks, but their performance in other languages is limited. |
| Approach: | They conducted an exhaustive analysis of the multilingual capability of LLMs by examining the performance gap before and after embedding fine-tuning across 101 languages. |
| Outcome: | The proposed model improves on the attributes of four quadrants in the model and provides actionable and efficient guidelines for tuning these languages. |
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)
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| Challenge: | Existing methods for enhancing the performance of large language models require expensive manual annotations. |
| Approach: | They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence. |
| Outcome: | The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method. |
MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification (2026.findings-acl)
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Jingwei Song, Xinyu Wang, Hanbin Wang, Xiaoxuan Lei, Tianyu Shi, Shixin Han, Eric Yang, Xiao-Wen Chang, Lynn Ai
| Challenge: | Autoregressive large language models suffer from high inference latency due to memorybandwidth constraints. |
| Approach: | They propose a method that decouples generation and verification by decoupling tokens and a lightweight draft model. |
| Outcome: | The proposed method delivers consistent and significant speedups over state-of-the-art baselines while preserving generation quality across diverse benchmarks. |
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing (2022.emnlp-demos)
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Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
| Challenge: | Pre-Trained Models (PTMs) have reshaped the development of natural language processing (NLP) but it is not easy to obtain high-performing PTMs without a large amount of labeled training data and deploy them online with fast inference speed. |
| Approach: | They propose to make it easy to build NLP applications with knowledge-enhanced pre-training and knowledge distillation. |
| Outcome: | EasyNLP supports a comprehensive suite of NLP algorithms and features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities. |
Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)
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| Challenge: | Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering. |
| Approach: | They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths . |
| Outcome: | The proposed method surpasses existing methods on knowledge-intensive multi-hop questions. |
Autoregressive Knowledge Distillation through Imitation Learning (2020.emnlp-main)
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| Challenge: | Autoregressive models are ubiquitous in natural language processing due to the sequential nature of text generation. |
| Approach: | They propose a compression technique for autoregressive models driven by an imitation learning perspective on knowledge distillation. |
| Outcome: | The proposed method outperforms other distillation algorithms on translation and summarization tasks while increasing inference speed 14 times. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark (2025.acl-long)
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Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui, Qinzheng Sun, Shaoguang Mao, Xin Zhang, Ying Xin, Qiufeng Yin, Scarlett Li, Furu Wei
| Challenge: | Multiple-choice question datasets like Massive Multitask Language Understanding (MMLU) have inevitably led to benchmark contamination, resulting in unreliable evaluation. |
| Approach: | They propose a contamination-free MCQ benchmark called MMLU-CF which reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination. |
| Outcome: | The proposed MMLU-CF reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination. |
KVPR: Efficient LLM Inference with I/O-Aware KV Cache Partial Recomputation (2025.findings-acl)
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| Challenge: | Existing methods to inference large language models are limited by CPU capabilities and memory constraints. |
| Approach: | They propose an efficient I/O-aware LLM inference method that overlaps GPU computation with KV cache transfer to minimize idle GPU time. |
| Outcome: | The proposed method reduces the cost of auto-regressive decoding by 35.8% . it also achieves 46.2% higher throughput during decoding compared to state-of-the-art methods. |
MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)
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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context. |
| Approach: | They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts. |
| Outcome: | The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context. |
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective (2025.coling-main)
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| Challenge: | Enabling LLMs to handle lengthy context is currently a research hotspot . a notable challenge limiting further customization is the inability of LLM to utilize context beyond pretrained length due to the inherent flaw of rotary position embedding (RoPE). |
| Approach: | They propose to extend the RoPE from an attention perspective and on two benchmarking tasks. |
| Outcome: | The proposed extension of the RoPE improves extrapolation and retrieval errors. |
Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering (2023.acl-long)
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| Challenge: | Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources. |
| Approach: | They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question. |
| Outcome: | The proposed framework outperforms SOTA methods on complex QA datasets. |
Learning Logic Rules for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing models for document-level relation extraction relied on implicitly powerful representations, which makes the model less transparent. |
| Approach: | They propose a probabilistic model for document-level relation extraction by learning logic rules. |
| Outcome: | The proposed model outperforms baseline models in relation performance and logical consistency. |
Prompt-Based Meta-Learning For Few-shot Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to learn text labels require large amounts of data to build many few-shot tasks. |
| Approach: | They propose a Prompt-Based Meta-Learning model that adds the prompting mechanism to the meta-learning method. |
| Outcome: | The proposed method improves on four text classification datasets with high accuracy and robustness. |
Enhancing Lexicon-Based Text Embeddings with Large Language Models (2025.acl-long)
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| Challenge: | Recent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks. |
| Approach: | They introduce the first lexicon-based embeddings that consolidates the vocabulary space through token embeddation clustering to handle the issue of token redundancy in LLM vocabularies. |
| Outcome: | The proposed model outperforms dense embeddings on the Massive Text Embedding Benchmark (MTEB) it also supports efficient dimension pruning without any specialized objectives like Matryoshka Representation Learning. |
Smart-Searcher: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning (2025.findings-emnlp)
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Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Xin Zhao, Lei Fang, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-augmented generation (RAG) helps by injecting external information, but current methods are costly, generalize poorly, or ignore the model’s internal knowledge. |
| Approach: | They propose a framework to train large language models to leverage both internal and external knowledge sources. |
| Outcome: | The proposed framework outperforms existing methods and achieves efficient retrieval-augmented reasoning. |
XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)
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| Challenge: | Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Approach: | They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Outcome: | The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales. |
PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents (2026.acl-long)
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Lei Wei, Xiao Peng, null Tt, Guannan Zhang, Chenhao Jiang, Hongyu Li, Lanbo Lin, Yuanwu Xu, Jiayao Liu, Kesu Wang, Bin Wang
| Challenge: | Large Language Model (LLM) agents struggle with ultra-long-horizon tasks requiring hundreds or thousands of interaction steps. |
| Approach: | They propose a framework that reconceptualizes context management as a Next Step Prediction problem. |
| Outcome: | The proposed framework improves task success rates and robust cross-lingual performance. |
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks (2024.eacl-long)
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| Challenge: | Existing approaches to automating ML are time-consuming and difficult to understand for human developers. |
| Approach: | They propose a framework that leverages large language models to develop ML solutions for novel tasks. |
| Outcome: | The proposed framework bridges the gap between machine intelligence and human knowledge by exploiting state-of-the-art large language models. |
Can Large Language Models Understand Internet Buzzwords Through User-Generated Content (2025.acl-long)
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| Challenge: | Existing methods for generating definitions of internet buzzwords rely on user-generated content, such as posts and reviews, to understand them. |
| Approach: | They propose a method to generate accurate buzzword definitions using UGC as examples. |
| Outcome: | The proposed method mirrors human language learning skills and can produce more accurate definitions. |
DT-QDC: A Dataset for Question Comprehension in Online Test (2020.coling-main)
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| Challenge: | Existing methods to measure difficulty of questions are not accurate enough to guide learning. |
| Approach: | They propose to use a Chinese DT-QDC dataset to measure difficulty of questions and provide a new model that can improve the judgment of difficulty from different perspectives. |
| Outcome: | The proposed methods outperform baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE, and 28.2% on MSC on the new DT-QDC dataset. |
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation (2022.findings-acl)
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| Challenge: | Existing pre-trained language models produce large sentence embeddings, resulting in performance gap between large and small models. |
| Approach: | They propose a method that augments a small Transformer encoder model with learnable projection layers to produce compact sentences while mimicking a large pre-trained language model to retain the sentence representation quality. |
| Outcome: | The proposed method achieves 2.7-4.5 points performance gain on STS and SR tasks while maintaining the quality of the pre-trained language models. |
Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (2023.emnlp-main)
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| Challenge: | Existing MWP solvers do not handle variants that can be derived via mathematical manipulation. |
| Approach: | They propose a non-autoregressive solver to present a solution expression and decode it from a given problem description. |
| Outcome: | The proposed solver is able to decode multiple expression variants and correct them . it is based on a unified tree structure and is available on Math23K and MAWPS. |
Representation-Guided Parameter-Efficient LLM Unlearning (2026.findings-acl)
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| Challenge: | Existing methods to unlearning large language models often memorize sensitive or harmful information, but they struggle with the forget-retain trade-off due to the polysemantic nature of LLMs parameters. |
| Approach: | They propose a representation-guided low-rank unlearning approach that leverages the geometric properties of representation spaces to achieve robust and precise unlearning. |
| Outcome: | The proposed approach outperforms state-of-the-art models on TOFU and WMDP benchmarks while maintaining higher model utility. |
ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents (2026.acl-long)
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| Challenge: | Existing approaches to measuring and optimizing proactive task-oriented agents lack generalizable end-to-end solutions. |
| Approach: | They propose a framework for conversational task scheduling that integrates proactiveness reinforcement learning with a domain-agnostic annotation methodology. |
| Outcome: | The proposed framework enables scalable proactiveness reinforcement learning (RL) Experiments on two newly auto-annotated datasets demonstrate significant improvements in proactive timing while maintaining action consistency comparable to state-of-the-art baselines. |
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)
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| Challenge: | Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing. |
| Approach: | They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs. |
| Outcome: | The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge. |
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)
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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks. |
| Approach: | They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions. |
| Outcome: | The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. |
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge (C18-1)
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| Challenge: | Existing approaches to answer selection are limited in domains with limited labeled data. |
| Approach: | They propose a Knowledge-aware Attentive Network framework for cross-domain answer selection that uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domain. |
| Outcome: | The proposed model outperforms strong competitors by a noticeable margin in cross-domain answer selection. |
Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Large vision-language models exhibit an imbalance in multilingual capabilities . |
| Approach: | They propose a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise Language Specific layers fine-tuning. |
| Outcome: | The proposed training recipe achieves efficient multilingual enhancement for LVLMs by fine-tuning language specific layers. |
Fusion-Eval: Integrating Assistant Evaluators with LLMs (2024.emnlp-industry)
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| Challenge: | Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks. |
| Approach: | They propose a method that leverages large language models to integrate insights from various assistant evaluators. |
| Outcome: | The proposed approach achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.7444 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods. |
Secoco: Self-Correcting Encoding for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Neural machine translation (NMT) is a challenging field due to the wide variety of noises in real-world scenarios. |
| Approach: | They propose a framework that explicitly deals with noisy inputs for robust neural machine translation by introducing self-correcting predictors. |
| Outcome: | The proposed framework can correct noisy inputs and delete specific errors with the translation decoding process. |
Open-Set Living Need Prediction with Large Language Models (2025.findings-acl)
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Xiaochong Lan, Jie Feng, Yizhou Sun, Chen Gao, Jiahuan Lei, Xinleishi Xinleishi, Hengliang Luo, Yong Li
| Challenge: | Existing approaches to living need prediction treat it as a closed-set classification problem, severely limiting their ability to capture diversity and complexity of living needs. |
| Approach: | They propose a system leveraging large language models for unrestricted need prediction that leverages Maslow's hierarchy of needs to align predictions with human living needs. |
| Outcome: | The proposed system outperforms closed-set approaches on need-based life service recall by an average of 19.37% on real-world datasets. |
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)
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| Challenge: | Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts. |
| Approach: | They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls. |
| Outcome: | The results show that the evaluations are reliable and lack pipeline evaluations. |
S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
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Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
| Challenge: | Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks. |
| Approach: | They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents . |
| Outcome: | The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%. |
Learning from the Irrecoverable: Error-Localized Policy Optimization for Tool-Integrated LLM Reasoning (2026.acl-long)
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| Challenge: | Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning suffers from sparse, delayed rewards and weak step-level credit assignment. |
| Approach: | They propose a tool-integrated reasoning approach that localizes the first irrecoverable step and leverages it for fine-grained credit assignment. |
| Outcome: | The proposed algorithm outperforms strong Agentic RL benchmarks in math, science QA, and code execution with additional gains in Pass@K and Major@K scaling, rollout ranking quality, and tool-call efficiency. |
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on evaluating single-round interactions, neglecting other critical aspects. |
| Approach: | They propose a benchmark to evaluate full-duplex speech language models in multi-round settings . they segment continuous full-dual dialogues into discrete turns for evaluation . |
| Outcome: | The proposed benchmark compared full-duplex speech language models with full-dual speech models . the results show that the models perform better in multi-round settings than standard models compared to benchmarks . |
Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding (2024.acl-long)
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| Challenge: | Generating long-term texts using artificial intelligence has always been a challenge . however, the generated novels exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. |
| Approach: | They propose a method for extracting excelsior and expanding from novel data to generate arbitrarily long novels using large language models. |
| Outcome: | The proposed method produces high-quality long-form novels with a high level of logical coherence and appeal despite the use of large language models. |
PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval (2025.findings-emnlp)
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| Challenge: | Current 3D medical imaging models focus on spatial features, neglecting phase-specific progression detailed in clinical reports. |
| Approach: | They propose a framework that fuses imaging phases with clinical text to enhance 3D medical image retrieval. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a phase-series dataset of 12,230 hospital CT scans. |
Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for large language modeling are based on task-related instructions or prompts. |
| Approach: | They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts. |
| Outcome: | The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts . |
Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
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| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
| Approach: | They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment . |
| Outcome: | The proposed method outperforms previous methods on diverse tasks. |
Graph Neural Network Enhanced Retrieval for Question Answering of Large Language Models (2025.naacl-long)
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| Challenge: | Existing retrieval methods divide reference documents into passages, treating them in isolation. Existing methods only use contiguous passages or keywords. |
| Approach: | They propose a retrieval method that leverages graph neural networks to exploit relatedness between passages to enhance retrieval. |
| Outcome: | The proposed method improves retrieval by exploiting the relatedness between passages. |
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu
| Challenge: | Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes. |
| Approach: | They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence. |
| Outcome: | The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show. |
DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)
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| Challenge: | Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets. |
| Approach: | They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments. |
| Outcome: | The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings. |
Neural Collective Entity Linking (C18-1)
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| Challenge: | Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently. |
| Approach: | They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency. |
| Outcome: | The proposed model improves its performance on five publicly available datasets and can be used to train on Wikipedia hyperlinks to avoid overfitting and domain bias. |
Dual Complex Number Knowledge Graph Embeddings (2024.lrec-main)
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| Challenge: | Existing knowledge graph embedding methods fail to model non-commutative composition patterns . extending to such sophisticated spaces increases the amount of parameters, which greatly reduces the parameter efficiency. |
| Approach: | They propose a new knowledge graph embedding method that maps entities to the dual complex number space and represents relations as rotations in 2D space via dual complex multiplication. |
| Outcome: | Experiments on multiple benchmark knowledge graphs show that the proposed method improves link prediction and path query answering. |
Differentially Private Natural Language Models: Recent Advances and Future Directions (2024.findings-eacl)
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| Challenge: | Recent advances in deep learning have led to great success in various natural language processing tasks. |
| Approach: | They propose a systematic review of recent advances in DP deep learning models . they discuss some differences and additional challenges of DP-NLP . |
| Outcome: | The proposed method can prevent reconstruction attacks and protect against potential side knowledge while maintaining the privacy of sensitive data. |
Distillation-Resistant Watermarking for Model Protection in NLP (2022.findings-emnlp)
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| Challenge: | Existing protection methods such as watermarking only work for images but are not applicable to text. |
| Approach: | They propose a technique that injects watermarks into the victim’s prediction probability corresponding to a secret key and is able to detect such a key by probing a suspect model. |
| Outcome: | The proposed technique detects stealing suspects at 100% accuracy on four NLP tasks while the prior method fails on two. |
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Existing studies ignore aspect terms interaction when labeling polarities . aspect terms extraction and aspect sentiment classification are two fundamental tasks . |
| Approach: | They propose a GRadient hArmonized and CascadEd labeling model to solve the imbalance issue . they extend the gradient harmonized mechanism used in object detection to aspect-based sentiment analysis . |
| Outcome: | The proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results. |
ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs (2026.findings-acl)
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| Challenge: | Existing methods rely on text retrieval and geographic knowledge bases to generate coordinates, and they are prone to error propagation and dependency on structured knowledge bases. |
| Approach: | They propose to use large language models to convert geographic coordinates into geohash sequences and introduce a Chain-of-Thought mechanism to enhance the model’s reasoning over spatial relationships. |
| Outcome: | The proposed framework can handle explicit address queries in single-point predictions and effectively resolve vague relative location queries. |
Translation Canvas: An Explainable Interface to Pinpoint and Analyze Translation Systems (2024.emnlp-demo)
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| Challenge: | Existing tools for evaluation of translation models focus on high-level metrics like BLEU or COMET scores, which are time-consuming and prone to error. |
| Approach: | They propose a toolkit that provides a detailed analysis of translation models and a user-friendly interface. |
| Outcome: | The toolkit shows superior performance over COMET and SacreBLEU packages under enjoybility and understandbility criteria. |
Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)
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| Challenge: | Pre-trained language models have demonstrated superior performance on various natural language processing tasks. |
| Approach: | They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training . |
| Outcome: | The neurons are highly predictive of task labels after prompt tuning for specific tasks. |
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have identified critical layers linked to specific functions or behaviors, limiting their use to post-hoc settings. |
| Approach: | They propose a data-oblivious approach to identify intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered Kernel Alignment. |
| Outcome: | The proposed approach identifies critical layers in pre-fine-tuned models . layers with significant shifts in representation space are also those most affected during fine-tuning . |
ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom (2025.emnlp-main)
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Jingqi Zhou, Sheng Wang, Jingwei Dong, Kai Liu, Lei Li, Jiahui Gao, Jiyue Jiang, Lingpeng Kong, Chuan Wu
| Challenge: | Large vision-language models often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation. |
| Approach: | They propose a visual reasoning framework that decouples vision-reasoning capabilities and multi-run proactive perception. |
| Outcome: | The proposed framework outperforms existing models on benchmarks for open-source and closed-source models with 13.2% performance gain. |
EMONA: Event-level Moral Opinions in News Articles (2024.naacl-long)
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Yuanyuan Lei, Md Messal Monem Miah, Ayesha Qamar, Sai Ramana Reddy, Jonathan Tong, Haotian Xu, Ruihong Huang
| Challenge: | Recent work on news articles has focused on social media short texts, but little has explored moral sentiment within news articles. |
| Approach: | They propose to extract event-level moral opinions from news articles using a new dataset . they use annotated event-based moral opinions to analyze news articles . |
| Outcome: | The proposed dataset consists of 400 news articles containing over 10k sentences and 45k events, among which 9,613 events received moral foundation labels. |
DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Search (2025.emnlp-main)
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| Challenge: | Existing frameworks for large language models with context length limitations are suboptimal for initialization and fine-tuning. |
| Approach: | They propose a RoPE-based fine-tuning framework that strategically determines the best scaling factors for LLMs by a Divide-and-Conquer Incremental Search algorithm. |
| Outcome: | The proposed framework mitigates performance decay at extended target lengths and can perform effectively without fine-tuning. |
Generative Prompt Internalization (2025.naacl-long)
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| Challenge: | Prompts used in large language model based applications are often fixed and lengthy, leading to significant computational overhead. |
| Approach: | They propose a method that internalizes complex prompts using a joint training approach and a data synthesis technique that auto-collects conversational datasets by swapping roles of agent and environment. |
| Outcome: | The proposed method internalizes complex prompts across agent-based applications and generates the content along with reasons for why it should change accordingly. |
FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)
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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang
| Challenge: | FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Approach: | They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. |
| Outcome: | The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download. |
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)
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Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo
| Challenge: | Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution. |
| Approach: | They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work. |
Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory (2026.acl-long)
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| Challenge: | Existing literature on dialog memory systems is inconsistent on their effectiveness . empirical findings on graph structures are difficult to attribute to specific design choices . |
| Approach: | They propose a framework that decomposes dialog memory systems into core components . they conduct stage-wise experiments on LongMemEval and HaluMeM, and compare implementation details . |
| Outcome: | The proposed framework compares graph-based and non-graph memory architectures on long-term dialog memory systems. |
Representational Isomorphism and Alignment of Multilingual Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing isomorphism of sentence representations can facilitate representational alignments in zero-shot and few-shot settings. |
| Approach: | They propose to apply a contrastive objective to LLMs with a small number of translation pairs to improve models' performance on Semantic Textual Similarity tasks. |
| Outcome: | The proposed representation-level approach significantly improves on Semantic Textual Similarity (STS) tasks across languages even without a monolingual objective. |
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)
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Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao
| Challenge: | Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms. |
| Approach: | They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content. |
| Outcome: | The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs. |
Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)
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| Challenge: | Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time. |
| Approach: | They propose a method that uses a pre-trained continuous vector to generate a fixed pre-trainable language model to satisfy a specified attribute. |
| Outcome: | The proposed model can achieve improvements on eleven attribute-specific generation tasks with 0.08% extra training parameters. |
Calibrating Factual Knowledge in Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing studies show that Pretrained Language Models can store factual knowledge, but facts stored in PLMs are not always correct. |
| Approach: | They propose a lightweight method to calibrate factual knowledge in PLMs without re-training from scratch. |
| Outcome: | The proposed method can be used to calibrate factual knowledge in PLMs without re-training from scratch. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)
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| Challenge: | Text-based question answering (TBQA) has been studied extensively in recent years. |
| Approach: | They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them. |
| Outcome: | The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains. |
Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets (2026.acl-long)
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| Challenge: | Existing mitigation strategies focus on reactively addressing jailbreak incidents after safety guardrails have been compromised. |
| Approach: | They investigate the degradation of safety guardrails through the lens of representation similarity between upstream alignment datasets and downstream fine-tuning tasks. |
| Outcome: | The proposed model reduces harmfulness score by 10.33% when compared to baseline models. |
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)
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Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li
| Challenge: | Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored. |
| Approach: | They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation. |
| Outcome: | The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs. |
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)
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| Challenge: | Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability. |
| Approach: | They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach. |
| Outcome: | The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios. |
Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation (2026.acl-long)
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Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao
| Challenge: | Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA . |
| Approach: | They propose a dual-LLM Questioner–Solver pipeline that integrates external knowledge from compilers and runtime feedback to generate verified translations and multi-turn dialogues. |
| Outcome: | The proposed model outperforms proprietary models on key metrics like compilation success and accuracy. |
Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment (2025.emnlp-main)
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| Challenge: | Recent studies show that fine-tuning with benign data can compromise safety of aligned LLMs. |
| Approach: | They propose a Layer-Aware Representation Filtering method that detects safety-degrading layers within the LLM and leverages their representations to detect them. |
| Outcome: | The proposed method can detect safety-degrading features in benign data and remove them from the model. |
A Survey on In-context Learning (2024.emnlp-main)
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui
| Challenge: | In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples . |
| Approach: | They propose to explore ICL to evaluate and extrapolate the ability of large language models. |
| Outcome: | The proposed methods can be used to evaluate and extrapolate the ability of large language models. |
DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction (2024.findings-emnlp)
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| Challenge: | Chinese document-level event extraction is still largely unexplored. |
| Approach: | They propose a Chinese document-level event extraction dataset with over 36,000 events and 210,000 arguments. |
| Outcome: | The proposed dataset includes over 36,000 events and more than 210,000 arguments . it is an extension of the DocEE dataset, utilizing the same event schema and annotated by human experts. |
NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models (2024.findings-acl)
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| Challenge: | Existing LLMs are not able to handle numerals and units of measurement, but they can be improved by introducing perturbations. |
| Approach: | They propose to analyze existing LLMs on processing numerals and units of measurement by perturbing their datasets. |
| Outcome: | The proposed model improves on ancient Chinese arithmetic problems and can handle numeral and measurement conversions. |
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)
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| Challenge: | Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation. |
| Approach: | They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information. |
| Outcome: | The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%. |
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)
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| Challenge: | Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable . |
| Approach: | They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark . |
| Outcome: | The proposed framework outperforms existing models in terms of performance and interpretability. |
Unsupervised Fine-tuning for Text Clustering (2020.coling-main)
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| Challenge: | Existing approaches to text clustering fine-tune pre-trained models have been limited. |
| Approach: | They propose a method to fine-tune pre-trained models unsupervisedly for text clustering by learning text representations and cluster assignments using a clustering oriented loss. |
| Outcome: | The proposed model outperforms baseline methods and achieves state-of-the-art results on three text clustering datasets. |
Explainable Text Classification with LLMs: Enhancing Performance through Dialectical Prompting and Explanation-Guided Training (2025.findings-emnlp)
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| Challenge: | Existing explanation methods that generate keywords may be less effective due to missing critical contextual information. |
| Approach: | They propose a new method to generate explanations for possible labels using LLMs and a dialectical prompt. |
| Outcome: | The proposed method significantly improves accuracy and explanation quality over state-of-the-art methods on multiple datasets from diverse domains. |
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning (2020.emnlp-main)
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| Challenge: | Walk-based models have shown their advantages in knowledge graph reasoning but are limited by their representations and generalizability. |
| Approach: | They propose a walk-based model that leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk- based agents. |
| Outcome: | Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability. |
Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition (2024.acl-long)
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| Challenge: | Recent work on dialogue-based collaborative plan acquisition (CPA) suggests Theory of Mind (ToM) modelling can improve missing knowledge prediction in settings with asymmetric skill-sets and knowledge. |
| Approach: | They propose to use task-specific constraints to represent plans as graphs and exploit task-related constraints to improve missing knowledge prediction in CPA. |
| Outcome: | The proposed model improves missing knowledge prediction in contexts with asymmetric skill-sets and knowledge, but the improvements diminish . the proposed model is compared with baseline models and found to be more effective than existing models. |
Contextual Representation Learning beyond Masked Language Modeling (2022.acl-long)
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| Challenge: | masked language models adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations. |
| Approach: | They propose a representation learning approach that uses embeddings as anchors to model contextual representations. |
| Outcome: | The proposed model achieves 5x speedup and 1.2 points average improvement over MLM. |
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure (2020.coling-main)
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| Challenge: | Multiparty dialog applications such as discourse parsing and meeting summarization are now mainstream research. |
| Approach: | They propose to annotate a machine reading comprehension dataset with discourse structure built over multiparty dialog using a modified Segmented Discourse Representation Theory (SDRT) style. |
| Outcome: | The proposed dataset contributes large-scale discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT) style for all of its multiparty dialogs, and achieves only 67.7% F1 on Molweni’s questions, a 20+% significant drop as compared against its SQuAD 2.0 performance. |
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration (2023.findings-emnlp)
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| Challenge: | Recent studies have shown that ChatGPT has limitations such as failing to ask clarifying questions to ambiguous queries or refusing problematic user requests. |
| Approach: | They propose a Proactive Chain-of-Thought prompting scheme which augments LLMs with the goal planning capability over descriptive reasoning chains to trigger proactivity. |
| Outcome: | The proposed scheme augments LLMs with the goal planning capability over descriptive reasoning chains to trigger the proactivity of LLM-based proactive dialogue systems. |
Dialogue Structure Annotation for Multi-Floor Interaction (L18-1)
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David Traum, Cassidy Henry, Stephanie Lukin, Ron Artstein, Felix Gervits, Kimberly Pollard, Claire Bonial, Su Lei, Clare Voss, Matthew Marge, Cory Hayes, Susan Hill
| Challenge: | Existing annotation schemes do not address dialogue structure. |
| Approach: | They propose an annotation scheme for meso-level dialogue structure that clusters utterances from multiple participants and floors into units according to realization of an initiator's intent. |
| Outcome: | The proposed annotation scheme is used to annotate a corpus of human-robot interaction dialogues. |
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)
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| Challenge: | Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB. |
| Approach: | They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities . |
| Outcome: | The proposed framework can achieve superior performance over the state of the arts. |
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media (2022.naacl-main)
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| Challenge: | Existing approaches focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. |
| Approach: | They propose a political perspective detection approach that leverages news text to enable multi-hop knowledge reasoning and incorporates textual cues as paragraph-level labels. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two benchmark datasets. |
ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks (2025.emnlp-main)
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| Challenge: | Multi-agent systems (MAS) are limited by poor flexibility and scalability, with underdeveloped optimization strategies. |
| Approach: | They propose a task graph generation and a reward-driven two-stage agent selection process to integrate multi-agent systems to improve their reasoning capabilities. |
| Outcome: | The proposed model outperforms existing methods on Math-MAS and SciBench-MAS SciBech, while other methods completely fail. |
Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding (2024.emnlp-main)
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| Challenge: | Existing models for GUI understanding ignore a key GUI-referring task: screen reading based on user-indicated points. |
| Approach: | They propose a Tree-of-Lens agent that constructs a Hierarchical Layout Tree based on user input points and a GUI screenshot. |
| Outcome: | The proposed agent can interpret the Screen Point-and-Read task on mobile, web, and operating systems. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
CoTrust: Privacy-Preserving Collaboration Between Large and Small Language Models in Trusted Execution Environments (2026.findings-acl)
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| Challenge: | Large language models (LLMs) provide powerful text generation capabilities, but accessing sensitive user inputs raises privacy concerns. |
| Approach: | They propose a privacy-preserving collaborative inference framework that combines large language models with small language models inside TEE to preserve privacy. |
| Outcome: | Experiments show that CoTrust outperforms unconstrained LLMs on multiple question answering and summarization benchmarks while maintaining strong privacy protection. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
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| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
| Outcome: | The proposed model learns primary TOD task completion skills from heterogeneous dialog corpora. |
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning (2022.findings-naacl)
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| Challenge: | Existing pre-trained MLMs produce an anisotropic distribution of token representations . this is not ideal for tasks that require discriminative semantic meanings of distinct tokens - a problem that exists in pre-training models . |
| Approach: | They propose a continual pre-training approach that encourages BERT to learn an isotropic distribution of token representations. |
| Outcome: | The proposed approach improves on a wide range of English and Chinese benchmarks. |
Identifying Conspiracy Theories News based on Event Relation Graph (2023.findings-emnlp)
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| Challenge: | Conspiracy theories are narratives that explains an event or situation in an irrational or malicious manner. |
| Approach: | They propose to integrate an event relation graph into conspiracy theory identification by using soft labels. |
| Outcome: | The proposed approach improves precision and recall of conspiracy theory identification, and generalizes well for new unseen media sources. |
Anticipating Future with Large Language Model for Simultaneous Machine Translation (2025.naacl-long)
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Siqi Ouyang, Oleksii Hrinchuk, Zhehuai Chen, Vitaly Lavrukhin, Jagadeesh Balam, Lei Li, Boris Ginsburg
| Challenge: | Existing methods only use the partial utterance that has already arrived at the input and the generated hypothesis. |
| Approach: | They propose to use a large language model to predict future source words and opportunistically translate without introducing too much risk. |
| Outcome: | The proposed method outperforms baselines on four language directions and achieves the best translation quality-latency trade-off by up to 5 BLEU points at the same latency. |
Beyond Inherent Cognition Biases in LLM-Based Event Forecasting: A Multi-Cognition Agentic Framework (2025.findings-emnlp)
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| Challenge: | Large Language Models exhibit human-like cognitive biases in event forecasting . a human-curated dataset reveals significant cognitive bias in LLMs . |
| Approach: | They propose a human-curated dataset to explore LLMs' cognitive biases . they leverage LLM participants to act as multi-cognition event participants . |
| Outcome: | The proposed framework alleviates cognitive biases in LLMs and offers diverse perspectives. |
Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers (2025.emnlp-main)
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| Challenge: | Autoregressive language models excel in text-to-audio generation, but lag behind diffusion models by a non-trivial margin. |
| Approach: | They propose a framework that integrates multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. |
| Outcome: | The proposed framework outperforms existing LM-based and diffusion-based systems in audio synthesis. |
Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation (2026.findings-acl)
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| Challenge: | Existing methods for training large language models rely heavily on high-quality parallel data, which are often scarce or unavailable for low-resource languages. |
| Approach: | They propose a reinforcement training method using only monolingual text to elevate LLMs’ translation capabilities on massive low-resource languages while retaining their performance on high-resourced languages. |
| Outcome: | The proposed model outperforms LLaMAX, one of the strongest open-source multilingual LLMs on 1,414 language directions on Flores-101 dataset. |
Pretraining the Noisy Channel Model for Task-Oriented Dialogue (2021.tacl-1)
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| Challenge: | Current research on task-oriented dialogue models suffers from the explaining-away effect, manifested in models that favor short and generic responses. |
| Approach: | They propose to factorize the dialogue task into two models, the distribution of the context given the response, and the prior for the response itself, using Bayes' theorem. |
| Outcome: | The proposed model mitigates the explaining-away effect and allows the principled incorporation of large pretrained models for the response prior. |
Marathon: A Race Through the Realm of Long Context with Large Language Models (2024.acl-long)
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| Challenge: | Existing long-context benchmarks do not accurately evaluate large language models’ comprehension and reasoning abilities in extended texts. |
| Approach: | They propose a new evaluation benchmark that adopts a multiple-choice question format and uses a multi-choke question format to assess the comprehension and reasoning skills of large language models. |
| Outcome: | The proposed benchmark provides a rapid, precise, and unbiased appraisal of the long-context comprehension skills of large language models. |
Length Controlled Generation for Black-box LLMs (2025.acl-long)
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Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Ting Liu, Bing Qin, Tat-Seng Chua
| Challenge: | Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use. |
| Approach: | They propose an iterative sampling framework that regulates LLMs to generate length-constrained text without modifying the underlying parameters. |
| Outcome: | The proposed method achieves 100% success rates on Llama3.1 tasks with minimal additional computational overhead. |
Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation (2026.findings-acl)
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| Challenge: | a new technology is transforming customer service chatbots into strategic bridges for business intelligence . a recent study shows that customer service bots are increasingly being used as reactive support tools . |
| Approach: | They propose a task of Proactive Information Probing which optimizes when to probe users for pre-specified information while minimizing conversation turns and user friction. |
| Outcome: | The proposed framework outperforms baselines in both information probing and service quality. |
LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models. |
| Approach: | They propose an end-to-end framework which Leverages Error Type (LET) information in the generation process to introduce more convincing error type information. |
| Outcome: | The proposed framework outperforms existing methods on various datasets by a clear margin. |
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)
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| Challenge: | Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance . |
| Approach: | They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences . |
| Outcome: | The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs. |
Cross-lingual Supervision Improves Unsupervised Neural Machine Translation (2021.naacl-industry)
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| Challenge: | Existing models that use only monolingual data have not been fully duplicated in the vast majority of language pairs, especially for zero-source languages. |
| Approach: | They propose to leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model. |
| Outcome: | The proposed model significantly improves translation quality with a big margin in the benchmark unsupervised translation tasks and achieves comparable performance to supervised NMT. |
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation (2023.emnlp-main)
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| Challenge: | Existing methods for question generation over knowledge bases rely on annotated data for fine-tuning . emergence of Large Language Models (LLMs) has shown impressive generalization ability in few-shot tasks. |
| Approach: | They propose to use a logical form to generate a question in a reasoning problem . they propose to extend the prompting method into a method that can generate questions in logical forms . |
| Outcome: | The proposed method outperforms baselines on three public KBQG datasets. |
Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding (2023.findings-acl)
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Mengyue Zhou, Xu Liu, David Liu, Zihao Wu, Zhengliang Liu, Lin Zhao, Dajiang Zhu, Lei Guo, Junwei Han, Tianming Liu, Xintao Hu
| Challenge: | Existing studies treat each transformer encoding layer as a single artificial neuron . layer-level embeddings aggregate multiple types of contextual attention captured by multiple head modules . |
| Approach: | They propose to embed each transformer encoding layer as a single artificial neuron . they propose to couple those ANs with their biological-neuron counterparts in the human brain . |
| Outcome: | The proposed models can be used to link representations to brain activity, the authors say . their results show that the proposed models carry meaningful neurolinguistic information . |
Discourse Structures Guided Fine-grained Propaganda Identification (2023.emnlp-main)
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| Challenge: | Using teacher-predicted probabilities and knowledge distillation frameworks to identify propaganda content is important. |
| Approach: | They propose to integrate local and global discourse structures for propaganda discovery and construct two teacher models for identifying PDTB-style discourse relations between nearby sentences and common discourse roles of sentences in a news article respectively. |
| Outcome: | The proposed models improve accuracy and recall of propaganda content identification at sentence-level and token-level. |
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)
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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo
| Challenge: | Recent studies have encountered limitations in leveraging large language models to generate symbolic world models. |
| Approach: | They propose a benchmarking framework based on planning domain definition language (PDDL) that employs multi-criteria, execution-based metrics for a more robust evaluation. |
| Outcome: | The proposed model outperforms models trained with large-scale reinforcement learning, but lacks the robustness needed to perform in world modeling. |
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models (2025.findings-acl)
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Haoyang Li, Xuejia Chen, Zhanchao Xu, Darian Li, Nicole Hu, Fei Teng, Yiming Li, Luyu Qiu, Chen Jason Zhang, Li Qing, Lei Chen
| Challenge: | Existing benchmarks focus on linguistic competence or structured mathematical problem-solving, neglecting fundamental numerical reasoning required in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate numerical capabilities for large language models . they use a dataset to assess number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
| Outcome: | The proposed benchmark evaluates six fundamental numerical capabilities: number recognition, arithmetic operations, contextual retrieval, comparison, summary, and multi-step reasoning. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Existing benchmarks evaluate contextual causal reasoning in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. |
| Approach: | They use a unified context to benchmark large language models' contextual causal reasoning skills. |
| Outcome: | The proposed benchmarks show that LLMs are susceptible to distraction by irrelevant but factually correct information at lower level of causality. |
GRAG: Graph Retrieval-Augmented Generation (2025.findings-naacl)
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| Challenge: | Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and is not suitable for networked documents. |
| Approach: | They propose a novel divide-and-conquer strategy that retrieves optimal subgraph structure in linear time. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on graph reasoning benchmarks. |
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization (2022.findings-acl)
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| Challenge: | Existing methods for few-shot text classification are limited by labeled data. |
| Approach: | They propose to use consistency regularization to improve few-shot text classification by generating pseudo-labels from weakly-augmented and strongly-augmented views. |
| Outcome: | The proposed method achieves competitive performance with 16 labeled examples with prompt and verbalizer. |
Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate (2026.findings-acl)
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| Challenge: | Existing approaches to evaluate large language models fail to address cultural bias in non-Western languages . Chinese prompting shifts bias toward East Asian perspectives rather than eliminating it, authors say . |
| Approach: | They propose a Chinese–English bilingual benchmark and multi-agent vote frameworks that enable explicit "no bias" judgments. |
| Outcome: | The proposed framework achieves 57.6% average No Bias Rate on Chinese-English benchmark and 86.0% on Arabic CAMeL benchmark. |
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)
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| Challenge: | In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples. |
| Approach: | They propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling. |
| Outcome: | The proposed pipeline reduces reliance on LLMs for data labeling . it leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances. |
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)
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Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun
| Challenge: | Existing legal event detection datasets only cover incomprehensive event types and have limited annotated data. |
| Approach: | They present a large-scale Chinese legal event detection dataset . they use legal events as side information to promote downstream applications . |
| Outcome: | The proposed method improves 2.2 points precision in low-resource judgment prediction and 1.5 points precision for unsupervised case retrieval. |
TVQA+: Spatio-Temporal Grounding for Video Question Answering (2020.acl-main)
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| Challenge: | Existing video QA datasets only contain QA pairs without labels for key clips or regions needed to answer the question. |
| Approach: | They propose a framework that grounds evidence in both spatial and temporal domains to answer questions about videos using bounding boxes. |
| Outcome: | The proposed framework can produce interpretable spatio-temporal attention visualizations. |
UCTG: A Unified Controllable Text Generation Framework for Query Auto-Completion (2025.coling-industry)
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| Challenge: | Existing approaches to control text generation (CTG) are essentially challenging to adapt to various control objectives and constraints, which results in mixed success. |
| Approach: | They propose a unified controllable text generation framework which integrates a control module, a prompt module, and a generation module. |
| Outcome: | The proposed framework significantly improves query accuracy and coherence in tasks with different objectives and constraints. |
Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance. |
| Approach: | They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning. |
| Outcome: | The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods. |
A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)
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| Challenge: | Context-dependent semantic parsing has proved to be an important but challenging task. |
| Approach: | They propose to perform follow-up query analysis to restate context-dependent queries with contextual information. |
| Outcome: | The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising . |
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)
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| Challenge: | Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity. |
| Approach: | They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation. |
| Outcome: | The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction. |
Pre-training Distillation for Large Language Models: A Design Space Exploration (2025.acl-long)
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| Challenge: | Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model for model compression. |
| Approach: | They extend knowledge distillation to the pre-training phase of large language models . they first conduct an experiment using a teacher LLM to distill a 1.9B student LLM . |
| Outcome: | The proposed model can be used to distill a 1.9B student model using a teacher LLM. |
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)
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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning. |
| Approach: | They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking. |
| Outcome: | The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
Training Language Models with Memory Augmentation (2022.emnlp-main)
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| Challenge: | Existing methods for training memory-augmented language models only introduce mem-ories at testing time or represent them using a separately trained encoder. |
| Approach: | They propose a training approach that directly takes in-batch examples as accessible memory and new methods for memory construction and data batching that are used for adapting to different sets of memories at testing time. |
| Outcome: | The proposed approach reduces perplexity from 18.70 to 15.37 on multiple language modeling and machine translation benchmarks. |
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)
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| Challenge: | Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages. |
| Approach: | They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. |
| Outcome: | The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages. |
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)
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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu
| Challenge: | Existing toolsets that use large language models are limited to single-task settings. |
| Approach: | They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. |
| Outcome: | The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. |
LayerNorm Induces Recency Bias in Transformer Decoders (2026.findings-acl)
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| Challenge: | Existing studies show that stacking causal self-attention layers alone induces a positional bias in attention scores toward earlier tokens, but this differs from the bias toward later tokens observed in Transformer decoders, known as recency bias. |
| Approach: | They propose to stack causal self-attention layers and layer norm to induce recency bias in Transformer decoders by analyzing the interaction between causal self and other architectural components. |
| Outcome: | The proposed method provides new theoretical insights into how positional information interacts with architectural components and suggests improvements in positional encoding strategies. |
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026.findings-acl)
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| Challenge: | Experimental results show that EasyRL consistently outperforms state-of-the-art baselines due to the substantial annotation cost and issues such as model collapse or reward hacking. |
| Approach: | They propose a supervised RL approach with a divide-and-conquer strategy that simulates the human cognitive acquisition curve using easy labeled data. |
| Outcome: | The proposed approach outperforms state-of-the-art models on mathematical and scientific benchmarks using only 10% of easy labeled data. |
PAGED: A Benchmark for Procedural Graphs Extraction from Documents (2024.acl-long)
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| Challenge: | Existing methods for extraction of procedural graphs from documents are not solving the task well. |
| Approach: | They propose a benchmark to test automatic extraction of procedural graphs from documents . they involve three advanced large language models and enhance them with a novel self-refine strategy . |
| Outcome: | The proposed benchmark systematically examines the progress of current methods and explores the potential of emerging large language models (LLMs) on this task. |
AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for making decisions in grounded environments require costly gradient computation or lengthy in-context demonstrations. |
| Approach: | They propose an approach to guide LLM-based agents to accomplish interactive decision-making tasks by using an LLM prompt and a task-solving plan. |
| Outcome: | The proposed approach outperforms human-written demonstrations on ALFWorld and HotpotQA by 8%. |
Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)
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| Challenge: | Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs. |
| Approach: | They propose a novel approach for joint answer prediction and proof generation using an induced graphical model. |
| Outcome: | The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions. |
Vocabulary Learning via Optimal Transport for Neural Machine Translation (2021.acl-long)
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| Challenge: | Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation. |
| Approach: | They propose a token dictionary solution that can be used without trial training to find the best dictionary with a proper size. |
| Outcome: | The proposed solution beats widely-used vocabularies in English-German translation, TED bilingual translation, and TED multilingual translation. |
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding (2023.acl-long)
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| Challenge: | Existing approaches to infuse knowledge graphs with pre-trained LMs are limited by the input sequence length. |
| Approach: | They propose a language model that leverages knowledge in local, document-level, and global contexts for long document understanding. |
| Outcome: | The proposed model achieves state-of-the-art on three long document understanding tasks across 6 datasets/settings. |
An Efficient Conversational Smart Compose System (2023.acl-demo)
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Yun Zhu, Xiayu Chen, Lei Shu, Bowen Tan, Xinying Song, Lijuan Liu, Maria Wang, Jindong Chen, Ning Ruan
| Challenge: | a cloud-based smart compose system is designed to improve human-to-human conversation efficiency. |
| Approach: | They propose a cloud-based smart compose system to improve conversation efficiency . they propose heuristics to achieve the best trade-off between quality and latency . |
| Outcome: | The proposed system reduces latency without losing composing quality further. |
x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)
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Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang, Zekun Yuan, Lei Huang, Weitao Ma, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing large language models (LLMs) ignore this diversity by reasoning in a single dominant language. |
| Approach: | They propose a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis. |
| Outcome: | The proposed model can reason in a single dominant language on a per-instance basis. |
Red Teaming Visual Language Models (2024.findings-acl)
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| Challenge: | VLMs (Vision-Language Models) can be induced to generate harmful or inaccurate content through specific test cases. |
| Approach: | They propose a red teaming dataset which encompasses 12 subtasks under 4 primary aspects (faithfulness, privacy, safety, fairness) this dataset is the first to benchmark current VLMs in terms of these 4 aspects . |
| Outcome: | The proposed dataset shows that 10 open-source VLMs struggle with red teaming in different degrees and have up to 31% performance gap with GPT-4V. |
Towards Harmonized Uncertainty Estimation for Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated exceptional capabilities in handling a wide range of downstream tasks. |
| Approach: | They propose a method that employs a lightweight model trained on data aligned with the target LLM’s performance to adjust uncertainty scores. |
| Outcome: | The proposed method achieves improvements of up to 60% over existing methods. |
METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues (2026.acl-long)
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| Challenge: | Developing non-collaborative dialogue agents traditionally requires manual codification of expert strategies. |
| Approach: | They propose a method that formalizes expert knowledge into a Strategy Forest from raw transcripts. |
| Outcome: | The proposed method outperforms existing methods by 9%-10% in two benchmarks. |
TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models (2026.acl-long)
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| Challenge: | Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance. |
| Approach: | They propose a communication-aware MoELoRA framework that relaxes this assumption by introducing expert-level communication prior to routing. |
| Outcome: | The proposed framework outperforms vanilla LoRA and MoELoRA on diverse language understanding tasks while maintaining expert dominance. |
Long-tailed Extreme Multi-label Text Classification by the Retrieval of Generated Pseudo Label Descriptions (2023.findings-eacl)
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| Challenge: | Extreme Multi-label text classification (XMTC) is a tough challenge due to the sheer size of the label spaces and the severe data scarcity problem associated with the long tail of rare labels in highly skewed distributions. |
| Approach: | They propose to use a trained bag-of-words classifier to generate pseudo label descriptions from a training bag- of-word classifier. |
| Outcome: | The proposed approach outperforms the existing models in the tail label prediction problem and achieves state-of-the-art (SOTA) performance on XMTC benchmark datasets. |
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data (2021.emnlp-demo)
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Vibhu Bhatia, Vidya Prasad Akavoor, Sejin Paik, Lei Guo, Mona Jalal, Alyssa Smith, David Assefa Tofu, Edward Edberg Halim, Yimeng Sun, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
| Challenge: | Existing frameworks for analyzing frames in multilingual text documents are available online and via an API. |
| Approach: | They propose a web-based system for analyzing frames in multilingual text documents . framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual language model . |
| Outcome: | The proposed framework can significantly improve frame prediction performance while requiring a small sample of manual annotations. |
Weighted Contrastive Learning With False Negative Control to Help Long-tailed Product Classification (2023.acl-industry)
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| Challenge: | Item categorization (IC) aims to classify a product into leaf nodes in a categorical taxonomy due to scarce supervision. |
| Approach: | They propose to use K-positive contrastive loss (KCL) to address IC task’s long-tail issue by re-weighting positive pairs in the KCL loss with a regularization that the sum of weights should be constrained to K+1 as close as possible. |
| Outcome: | The proposed method improves on the long-tail issue in the image classification task and when using text-based contrastive learning, it can be applied on the IC task. |
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models (2022.emnlp-main)
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| Challenge: | Existing statically compressed pre-trained language models lack spatial and temporal efficiency due to their large size and wide width. |
| Approach: | They propose a spatially and temporally efficient model which retains the major capacity of PLMs. |
| Outcome: | The proposed model retains the major capacity of pre-trained language models at high compression and acceleration rate with 1/8 parameters and 1/19 FLOPs of BERT. |
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability (2025.emnlp-main)
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| Challenge: | Existing methods to extend context length of Large Language Models (LLMs) still struggle with retrieval and reasoning in long context inputs. |
| Approach: | They propose a coarse-to-fine method to enhance multi-document question-answering capacities by removing background and distracting documents. |
| Outcome: | Experiments show that CAFE outperforms baseline methods on multiple documents. |
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are susceptible to generating harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking. |
| Approach: | They propose an end-to-end generative approach for jailbreak rewriting inspired by diffusion models that uses a sequence-tosequence (seq2sequ) diffusion model as a generator, conditioning on the original prompt and guiding the denoising process with a novel attack loss. |
| Outcome: | Experiments on Advbench and Harmbench show that the proposed method outperforms autoregressive jailbreak models across evaluation metrics including ASR, fluency, diversity and diversity. |
Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing methods to learn temporal evolutional representations of entities are hard to capture the complex temporal patterns such as sequential and repetitive. |
| Approach: | They propose a Sequential and Repetitive Pattern Learning method that captures both sequential and repetitive patterns. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four representative benchmarks on GDELT dataset, where performance improvement of MRR reaches up to 18.84%. |
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents (2020.acl-main)
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| Challenge: | Existing methods for entity and relation extraction require light human annotation efforts. |
| Approach: | They propose a method to re-label noisy instances with a cooperative group . they use a confidence consensus module to gather the wisdom of all agents . |
| Outcome: | The proposed model outperforms state-of-the-art methods on two real-world datasets. |
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)
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| Challenge: | Faceted summarization provides briefings of a document from different perspectives. |
| Approach: | They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents . |
| Outcome: | The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains. |
DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search (2026.acl-long)
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Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai, Lei Cao
| Challenge: | Existing methods to train large language models overlook quality of intermediate search results . existing methods often invoke search calls during reasoning, making inference inefficient . |
| Approach: | They propose a dual-objective reinforcement learning framework to improve search strategies of MLLMs . DORA outperforms state-of-the-art methods, achieving up to 8.4% higher accuracy . |
| Outcome: | The proposed model outperforms state-of-the-art methods while reducing search calls by 9.7%. |
Re-examining the Role of Schema Linking in Text-to-SQL (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL models treat schema linking as a minor component . Existing solutions treat schema as merely a string component based on string matching . |
| Approach: | They build a schema linking corpus based on a Spider text-to-SQL dataset . they find schema linking is the crux for the current text- to-Sql task . |
| Outcome: | The proposed model performs well on the Spider text-to-SQL dataset despite its simplicity. |
A Simple yet Effective Learnable Positional Encoding Method for Improving Document Transformer Model (2022.findings-aacl)
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| Challenge: | Existing document Transformers lack a robust positional encoding mechanism to indicate and embed sequential order information in documents. |
| Approach: | They propose a positional encoding method that can be pre-trained on document datasets to improve document understanding. |
| Outcome: | The proposed method outperforms baselines on document understanding tasks in form, receipt, and invoice domains and is robust and stable on noisy data with incorrect order information. |
Dense Procedure Captioning in Narrated Instructional Videos (P19-1)
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| Challenge: | Existing models for video dense captioning learn video segments and generate captions without considering transcripts. |
| Approach: | They propose a model to generate procedure captions from narrated instructional videos . they extract procedures by a cross-modality module and generate captions by encoding video frames and transcripts within each extracted procedure. |
| Outcome: | The proposed model can extract procedures from narrated instructional videos and generate procedure captions by encoding video frames and transcripts. |
Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection (2020.acl-main)
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| Challenge: | Existing methods for identifying controversial posts on social media are limited . existing methods fail to incorporate semantic information from content-related posts . |
| Approach: | They propose a method to integrate the information from topics, posts, and comments . they extend their model to Disentangled TPC-GCN to disentangle topic-related features . |
| Outcome: | The proposed method outperforms existing methods on two real-world datasets. |
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)
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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge. |
| Approach: | They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass. |
| Outcome: | The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively. |
Structured Pruning of Large Language Models (2020.emnlp-main)
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| Challenge: | Recent advances in language modeling have led to remarkable improvements on a variety of tasks. |
| Approach: | They propose a generic, structured pruning approach by parameterizing each weight matrix and adaptively removing rank-1 components during training. |
| Outcome: | The proposed method outperforms unstructured pruning and block pruning on language modeling tasks while achieving speedups during training and inference. |
Graph-to-Tree Learning for Solving Math Word Problems (2020.acl-main)
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| Challenge: | Existing tree-based neural models do not capture the relationships and order information among the quantities well. |
| Approach: | They propose a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two available datasets significantly. |
TVQA: Localized, Compositional Video Question Answering (D18-1)
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| Challenge: | Recent studies have focused on image-based question-answering (QA) tasks, but little has been done on video-based QA. |
| Approach: | They present a large-scale video QA dataset based on 6 popular TV shows . they provide analysis of the new dataset and trainable neural network framework . |
| Outcome: | The proposed dataset includes 152,545 QA pairs from 21,793 clips spanning over 460 hours of video. |
Syntactically Robust Training on Partially-Observed Data for Open Information Extraction (2022.findings-emnlp)
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| Challenge: | Open Information Extraction models have shown promising results with sufficient supervision, but the syntactic distribution of training data is partially observable in comparison to the real world. |
| Approach: | They propose a syntactically robust training framework that enables models to be trained on a multi-paraphrase distribution based on diverse paraphrase generation. |
| Outcome: | The proposed framework can be applied to other syntactic partial observable domains. |
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples (2024.naacl-long)
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| Challenge: | Existing methods for extracting aspects and opinions from text are incomplete. |
| Approach: | They propose a method for extracting Implicit Aspects with Categories and Opinions with Sentiments using implicit tokens. |
| Outcome: | The proposed method outperforms baseline methods on two public benchmark datasets. |
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
| Approach: | They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story. |
| Outcome: | The proposed approach outperforms existing methods in 84.33% of the trials. |
Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)
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Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang
| Challenge: | Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue. |
| Approach: | They propose to use a large language model (LLM) to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates. |
| Outcome: | The proposed method breaks brute-force combinations of retrievers with LLMs and lifts the performance of zero-shot retrieval to be very competitive on benchmark datasets. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie
| Challenge: | Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling. |
| Approach: | They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. |
| Outcome: | The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks. |
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL (2024.acl-long)
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Yunseon Choi, Sangmin Bae, Seonghyun Ban, Minchan Jeong, Chuheng Zhang, Lei Song, Li Zhao, Jiang Bian, Kee-Eung Kim
| Challenge: | Prompt tuning is an important technique for directing model behaviors and eliciting desired responses. |
| Approach: | They propose to find optimal prompt tokens using soft Q-learning to optimize models for prompt tuning. |
| Outcome: | The proposed method improves on baseline prompt tuning, and the results are more natural and interpretable. |
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)
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| Challenge: | Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. |
| Approach: | They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. |
| Outcome: | The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%. |
Exploring Generative Process Reward Modeling for Semi-Structured Data: A Case Study of Table Question Answering (2026.eacl-short)
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| Challenge: | Recent advances in process reward models (PRMs) have demonstrated remarkable improvements in the reasoning capabilities of large language models (LLMs). |
| Approach: | They evaluate state-of-the-art generative PRMs on table question answering from answer and step perspectives and compare their results to previous studies. |
| Outcome: | The proposed model can aid solution selection but struggle to generalize to out-of-domain data. |
Learning to Select In-Context Demonstration Preferred by Large Language Model (2025.findings-acl)
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| Challenge: | In-context learning (ICL) enables large language models to perform tasks with only a few examples as demonstrations. |
| Approach: | They propose a generative preference learning framework that leverages LLM feedback to directly optimize demonstration selection for ICL. |
| Outcome: | Experiments on 19 datasets across 11 task categories show that GenICL achieves superior performance than existing methods in selecting the most effective demonstrations. |
OTExtSum: Extractive Text Summarisation with Optimal Transport (2022.findings-naacl)
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| Challenge: | Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary. |
| Approach: | They propose to formulate extractive text summarisation as an Optimal Transport (OT) problem and use it to obtain an optimal summary that minimises the transportation cost to a given document. |
| Outcome: | The proposed method outperforms state-of-the-art methods and learning-based methods on multiNews, PubMed, BillSum, and CNN/DM datasets. |
Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)
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Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo
| Challenge: | Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. |
| Approach: | They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance. |
| Outcome: | The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50. |
QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)
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Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo
| Challenge: | Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving. |
| Approach: | They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch. |
| Outcome: | The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput. |
Half-S: Halving the Scale for Near-Lossless 4-Bit LLM Training (2026.findings-acl)
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Jinyang Du, Ruihao Gong, Linghan Ai, Zining Wang, Yunke Peng, Yao Wang, Lei Yan, null Wxuefei, Yaoyuan Wang, Jinyang Guo, Dahua Lin, Xianglong Liu
| Challenge: | Existing 4-bit training pipelines rely on max-scaling, which causes representation collapse . despite this, there are limitations in the accuracy of 4-bit LLM training . |
| Approach: | They propose a scaling strategy that uses half-scaling as a hardware-friendly default . they propose fp4 support that allows for a faster scaling of large language models . |
| Outcome: | The proposed scaling strategy narrows the gap between theoretical optimum and BF16 while maintaining the efficiency benefits of 4-bit training. |
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning (2023.emnlp-main)
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| Challenge: | In-context learning (ICL) is a promising capability for large language models (LLMs) but its underlying mechanism remains unexplored. |
| Approach: | They propose a demonstration compression technique to expedite inference and an analysis framework for diagnosing ICL errors in GPT2-XL. |
| Outcome: | The proposed method improves ICL performance and expedites inference. |
TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of Behavioral Therapy Notes (2025.acl-industry)
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| Challenge: | Behavioral therapy notes are important for legal compliance and patient care, but quality standards for them remain underdeveloped. |
| Approach: | They propose a rubric for evaluating therapy notes across key dimensions: completeness, conciseness, faithfulness. |
| Outcome: | The proposed evaluation framework improves on therapist-written notes and LLM-generated notes. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
Improving Retrospective Language Agents via Joint Policy Gradient Optimization (2025.naacl-long)
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| Challenge: | Recent advances in large language models have sparked interest in creating autonomous agents. |
| Approach: | They propose a framework that jointly optimizes both task-planning and self-reflective evolution capabilities in language agents. |
| Outcome: | The proposed framework improves task planning and self-reflective evolution capabilities in language agents. |
QDTSynth: Quality-Driven Formal Theorem Synthesis for Enhancing Proving Performance of LLMs (2025.acl-long)
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| Challenge: | Existing formal languages such as Lean, Coq and Metamath are proving to be useful in formal theorem proving . however, there is a scarcity of high-quality supervised fine-tuning data for formal proofs . |
| Approach: | They propose a Q**uality-**D**riven **T**heorem **S**ynthesis method in Lean4 . they propose diversity screening and the self-assessment method to select theoremas that exhibit diversity and high quality from the initial synthetic statements. |
| Outcome: | The proposed method significantly improves performance of open-source LLMs in theorem proving tasks. |
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)
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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community . |
| Approach: | They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction. |
| Outcome: | The proposed framework is open-source and available on Github. |
OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction (2026.acl-demo)
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| Challenge: | Existing MAS frameworks lack standardized abstractions, leading to low efficiency and repetitive implementation of core functions. |
| Approach: | They propose an open-source framework that encapsulates agents, tools, and reasoning flows as pluggable atomic components. |
| Outcome: | The OxyGent framework provides a robust and scalable foundation for multi-agent systems in industrial environments. |
FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining (2025.acl-long)
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| Challenge: | Existing approaches to training LLMs with Mixture-of-Experts (MoE) architecture on long sequences are limited by the insufficient computation. |
| Approach: | They propose a MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining. |
| Outcome: | The proposed system achieves 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines on GPT-MoE models with sequences up to 32K tokens. |
Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations (2024.naacl-long)
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| Challenge: | Pre-trained vector representations can inadvertently encode undesirable social biases. |
| Approach: | They propose a framework for reducing bias by transforming vector representations to an unbiased subspace using sufficient projection. |
| Outcome: | The proposed framework mitigates bias across debiasing and fairness tasks and across various vector representation types, including word embeddings and output representations of transformer models. |
Label-Guided Learning for Item Categorization in e-Commerce (2021.naacl-industry)
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| Challenge: | a recent study shows that item categorization uses the semantic information of the labels to guide the classification task. |
| Approach: | They investigate whether using the semantic information of the labels can improve item categorization systems in e-commerce. |
| Outcome: | The proposed methods improve item categorization performance on a real data set from a major e-commerce company in Japan. |
Dynamic Voting for Efficient Reasoning in Large Language Models (2023.findings-emnlp)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Xingzhang Ren, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Multi-path voting methods generate multiple reasoning paths for each problem, causing factual errors and illusion generation. |
| Approach: | They propose a multi-path voting technique that effectively reduces the number of reasoning paths during multi-path voting while preserving accuracies. |
| Outcome: | The proposed method outperforms Self-consistency using 24.7% of the number of paths on the LetterConcat task. |
SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning (2022.emnlp-main)
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| Challenge: | Existing methods for multi-hop knowledge graph reasoning suffer from slow and poor convergence . a transformer model can be used to learn and predict in an end-to-end fashion, giving faster convergence compared to previous methods . |
| Approach: | They propose a Sequence-to-sequence based multi-hop reasoning framework . it uses an encoder-decoder transformer structure to translate the query to a path . |
| Outcome: | The proposed framework can learn and predict in an end-to-end fashion, which gives better and faster convergence. |
MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for multimodal reasoning in large multimodal models are underperforming on multimodal tasks. |
| Approach: | They propose a benchmark for multimodal reasoning in large multimodal models, MM-MATH . MM's process evaluation employs LMM-as-a-judge to automatically analyze solution steps . diagram misinterpretation is the most common error, they find . |
| Outcome: | The proposed model achieves only 31% accuracy, compared to 82% for humans. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
A Systematic Investigation of KB-Text Embedding Alignment at Scale (2021.acl-long)
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| Challenge: | Knowledge bases (KBs) and text often contain complementary knowledge. |
| Approach: | They propose a framework for aligning KB and text embeddings for joint reasoning . they also evaluate alignment methods to infuse textual information into KB embeddables . |
| Outcome: | The proposed framework can be used to predict link prediction on emerging entities and events using textual information. |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. |
| Approach: | They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup. |
| Outcome: | The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer. |
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks (D19-1)
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| Challenge: | Existing methods for inferring the fine-grained type of an entity from knowledge base are incomplete and lack type information. |
| Approach: | They propose a novel Deep Learning architecture to infer the fine-grained type of an entity from a knowledge base. |
| Outcome: | The proposed method significantly outperforms four state-of-the-art methods on two large-scale datasets. |
Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards (2026.acl-long)
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| Challenge: | Existing agentic training data are narrow in task variety and easily solved . real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes. |
| Approach: | They propose a framework that synthesizes diverse tool-use training data and simulates complete environments. |
| Outcome: | The proposed framework synthesizes diverse tool-use training data and simulates complete environments. |
Dynamic PMI-Guided Contrastive Decoding Reduces Hallucination in Large Language Models: A Unified Framework of Fine-Grained Input Transformations (2026.findings-acl)
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| Challenge: | Despite the remarkable generation capabilities of large language models, the issue of hallucination remains a critical challenge. |
| Approach: | They propose a contrastive decoding framework based on dynamic pointwise mutual information that disentangles spurious dependencies induced by context priors, lexical co-occurrences, and syntactic structures and prioritizes causal logic. |
| Outcome: | The proposed framework significantly improves the model’s factuality and reasoning robustness while maintaining high computational efficiency. |
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph (2021.emnlp-main)
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| Challenge: | Existing models infer the answer by predicting the sequential relation path or aggregating the hidden graph features. |
| Approach: | They propose a model which jumps between entities at multiple steps . they demonstrate that TransferNet surpasses state-of-the-art models by a large margin . |
| Outcome: | The proposed model surpasses state-of-the-art models on MetaQA and on other datasets. |
Doolittle: Benchmarks and Corpora for Academic Writing Formalization (2023.emnlp-main)
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Shizhe Diao, Yongyu Lei, Liangming Pan, Tianqing Fang, Wangchunshu Zhou, Sedrick Keh, Min-Yen Kan, Tong Zhang
| Challenge: | Existing methods of language refinement focus on narrow, specific linguistic features within isolated sentences, such as grammatical errors and improper word use. |
| Approach: | They propose a task to improve the overall quality of academic writing at paragraph level by integrating automatic feedback into the training process. |
| Outcome: | The proposed task improves the overall quality of formal academic writing at the paragraph level. |
Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have been successful in Text-to-SQL tasks, but their deployment in real-world environments is hindered by latent reliability issues. |
| Approach: | They propose a framework to autonomously uncover latent failure patterns in LLM-based Text-to-SQL generation. |
| Outcome: | The proposed framework uncovers a substantial number of failure cases on state-of-the-art open-source LLMs. |
Brittle Minds, Fixable Activations: Understanding Belief Representations in Language Models (2025.findings-emnlp)
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| Challenge: | Despite growing interest in Theory of Mind (ToM) tasks for evaluating language models, little is known about how LMs internally represent mental states of self and others. |
| Approach: | They propose to investigate how LMs internally represent mental states of self and others . |
| Outcome: | The proposed model size and finetuning significantly improve LMs’ internal representations of others’ beliefs, which are structured - not mere by-products of spurious correlations - yet brittle to prompt variations. |
S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)
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| Challenge: | Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities . |
| Approach: | They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner. |
| Outcome: | The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard. |
GTM: A Generative Triple-wise Model for Conversational Question Generation (2021.acl-long)
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| Challenge: | Experimental results show that opendomain conversational question generation improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines. |
| Approach: | They propose a triple-wise model with hierarchical variations for open-domain conversational question generation using a post-question-answer triple and one-to-many semantic mappings. |
| Outcome: | The proposed model significantly improves the quality of questions in terms of fluency, coherence and diversity over baselines. |
SOAR: Supervision from Observation for Agentic Reinforcement Learning (2026.acl-long)
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| Challenge: | Prior work assigns supervision based on outcome rewards or external reward models, but ignores environment observations, a critical source of learning. |
| Approach: | They propose a supervision-based agentic reinforcement learning system that integrates environment observations as an explicit supervision signal. |
| Outcome: | The proposed model improves performance on reasoning and deep research tasks while reducing erroneous and inefficient tool usage. |
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)
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| Challenge: | Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings. |
| Approach: | They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context. |
| Outcome: | The proposed method performs better than translating the entire headline to the source language . it can be scaled up to many languages, even those without existing translation technologies . |
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics (2024.findings-naacl)
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| Challenge: | Existing studies have recognized hallucination as a notable concern in large autoregressive language models (LLMs). |
| Approach: | They propose a polygraph for large language models that detects "hallucination" they demonstrate that hallucination can be detected by tractable probabilistic models . |
| Outcome: | The proposed model outperforms state-of-the-art methods on open-source LLMs by 20% on TruthfulQA benchmarks. |
ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming (2025.acl-long)
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| Challenge: | Existing studies on human-LLM competitive programming use scattered, application-specific human feedback. |
| Approach: | They propose a taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation. |
| Outcome: | The proposed benchmark pinpoints strengths and weaknesses of existing methods and will be openly released. |
Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)
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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li
| Challenge: | Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. |
| Approach: | They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching. |
| Outcome: | The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions. |
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction (2023.findings-emnlp)
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| Challenge: | Existing studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty. |
| Approach: | They propose to extract crime amounts from fact description and use them to learn distinguishable representations to exploit the numbers in the fact description for predicting the term of penalty. |
| Outcome: | The proposed method achieves state-of-the-art results on real-world datasets and ablation studies demonstrate the effectiveness of each component. |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)
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| Challenge: | Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations. |
| Approach: | They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities. |
| Outcome: | The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types. |
AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant (2022.findings-emnlp)
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| Challenge: | Currently, personal AI assistants on the phone and AR glasses can assist our daily life in addressing our questions like "how to adjust the date for this watch?" |
| Approach: | They propose a task that asks a question about affordance of items in our daily life . they construct a dataset that contains 3.2k multimodal questions on 1.6k video segments . |
| Outcome: | The proposed task outperforms baseline methods while still having room for improvement in the future. |
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis (2022.naacl-main)
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| Challenge: | Existing approaches to classify aspects with aspect sentiment bias are hard to find . |
| Approach: | They propose a no-aspect differential sentiment framework for the ABSA task that eliminates aspect sentiment bias and uses differential sentiment loss instead of cross-entropy loss to better classify the sentiments. |
| Outcome: | The proposed framework can be combined with almost all traditional ABSA methods. |
latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)
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| Challenge: | Recent advances in text generation have limited applications due to multimodality problem. |
| Approach: | They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem. |
| Outcome: | The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm. |
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models (2022.findings-emnlp)
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| Challenge: | Existing models for natural language processing (NLP) are fine-tuned and released for research and deployments. |
| Approach: | They propose a PLM reuse paradigm that merges teacher-PLM knowledge into a student model. |
| Outcome: | The proposed paradigm can reduce the computational cost and environmental side-effects of retraining the PLM from scratch. |
SafetyMem: Adaptive Jailbreak Defense via Dual-Component Safety Memory (2026.acl-long)
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| Challenge: | Existing defenses for Large Language Models suffer from a 'memory gap' parameter-modifying methods are computationally expensive and inference-time filters cannot retain or reuse defense knowledge across interactions. |
| Approach: | They propose a framework that secures Large Language Models through a dual-component safety memory system. |
| Outcome: | The proposed framework significantly reduces attack success rates while preserving interpretability and efficiency. |
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing MMKGC methods overlook the imbalance problem of modality information among entities, resulting in inadequate modal fusion and inefficient utilization of the raw modality. |
| Approach: | They propose to integrate structural, visual, and textual information of entities into the discriminant models to predict the missing triples. |
| Outcome: | The proposed model outperforms 19 recent methods and achieves state-of-the-art results on three public MMKGC benchmarks. |
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training (2023.findings-acl)
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| Challenge: | Dense retrievers have impressive performance, but their demand for abundant training data limits their application scenarios. |
| Approach: | They propose a method which uses unlabeled data to construct pseudo-positive examples from unlabelled data and then contrastively weighs the contrastive loss of different pairs according to the estimated relevance. |
| Outcome: | The proposed method beats the SOTA unsupervised Contriever model on BEIR and open-domain QA retrieval benchmarks and is a good few-shot learner. |
FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)
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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao
| Challenge: | Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge . |
| Approach: | They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance. |
| Outcome: | The proposed model improves performance on e-commerce image-text retrieval task by a large margin. |
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)
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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong
| Challenge: | Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes. |
| Approach: | They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer. |
| Outcome: | The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks. |
S2ST-Omni: Hierarchical Language-Aware SpeechLLM Adaptation for Multilingual Speech-to-Speech Translation (2026.findings-acl)
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| Challenge: | S2ST-Omni integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend. |
| Approach: | They propose a compositional S2ST framework that integrates a speech-to-text frontend with a modular, plug-and-play text-tospeech backend. |
| Outcome: | The proposed framework outperforms existing frameworks in translation and synthesis . it integrates a speech-to-text translation frontend with a plug-and-play text-tospeech backend . |
When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute (2021.emnlp-main)
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| Challenge: | Large language models are difficult to train because of the growing computation time and cost. |
| Approach: | They propose a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. |
| Outcome: | The proposed model achieves state-of-the-art on a Wiki-103 and Billion Word datasets using 1.6 days of training on an 8-GPU machine. |
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)
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Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases. |
| Approach: | They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities. |
| Outcome: | The proposed model outperforms open-source models but struggles on longer contexts. |
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)
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| Challenge: | Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models. |
| Approach: | They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs. |
| Outcome: | The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought. |
ODIST: Open World Classification via Distributionally Shifted Instances (2021.findings-emnlp)
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| Challenge: | Existing work to achieve open-world classification capability in natural language processing and computer vision focuses on decision boundary finding. |
| Approach: | They propose a method that can create out-of-domain instances from in-domain training instances with the help of a pre-trained generative language model. |
| Outcome: | The proposed method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. |
Speeding Up Neural Machine Translation Decoding by Cube Pruning (D18-1)
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| Challenge: | Neural machine translation suffers from slow translation speed due to the large search space . a trade-off has to be made between translation quality and speed, argues a new study . |
| Approach: | They apply cube pruning technique to speed up dynamic programming into neural machine translation to speed it up. |
| Outcome: | The proposed method can translate faster on GPUs and CPUs with better translation quality than naive beam search. |
UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)
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Zhanyue Qin, Haochuan Wang, Deyuan Liu, Ziyang Song, Cunhang Fan, Zhao Lv, Jinlin Wu, Zhen Lei, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing LLMs demonstrate powerful capabilities between tasks, but can they make sequential decisions? |
| Approach: | They propose to evaluate sequential decision-making capability of large language models (LLMs) using novel metrics based Monte Carlo methods. |
| Outcome: | The proposed benchmark improves sequential decision-making performance compared to the vanilla LLM player. |
DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)
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| Challenge: | Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships. |
| Approach: | They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions. |
| Outcome: | The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions. |
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)
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Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao
| Challenge: | Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness . |
| Approach: | They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization. |
| Outcome: | The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets. |
CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation (2023.findings-acl)
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| Challenge: | End-to-end speech-totext translation (ST) data are limited due to the limited resources. |
| Approach: | They propose a knowledge distillation framework for speech translation that integrates knowledge from machine translation and decouples knowledge from non-target class knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a benchmark dataset. |
ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs (2024.findings-acl)
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| Challenge: | Existing approaches to integrate large language models and knowledge graphs with LLMs often ignore the rich cognitive potential inherent in KGs. |
| Approach: | They propose an observation-driven agent framework that integrates KG reasoning abilities via global observation and integrates it into the action and reflection modules. |
| Outcome: | The proposed framework improves on several datasets and achieves 12.87% and 8.9% accuracy improvements. |
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation (2021.emnlp-main)
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| Challenge: | Existing models for text-to-text generation do not explicitly focus on important concepts in the input and output. |
| Approach: | They propose a framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. |
| Outcome: | The proposed framework performs comparably or better than its unconstrained counterpart on automatic metrics and receives better ratings in the human evaluation. |
TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)
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Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao
| Challenge: | Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines. |
| Approach: | They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments. |
| Outcome: | The proposed metric has higher consistency with human judgments and is more accurate than existing metrics. |
Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents (2026.findings-acl)
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Huangwei Chen, Wu Li, Junhao Jia, Yining Chen, Xiaotao Pang, Ya-Long Chen, Li Gonghui, Haishuai Wang, Jiajun Bu, Lei Wu
| Challenge: | Initial outpatient consultations are costly and difficult to scale to real-time intake. |
| Approach: | They propose a synchronous virtual MDT framework that formalizes the consultation state using a structured SOAP representation, separating evidence collection from diagnostic reasoning to improve traceability and bias control. |
| Outcome: | The proposed framework outperforms state-of-the-art models on ClinicalBench and a real-world RAPID-IPN dataset in documentation quality and consultation capability. |
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals (2023.findings-acl)
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| Challenge: | Existing methods for machine reading comprehension of user manuals have trouble answering complex questions. |
| Approach: | They propose a knowing-how & knowing-that task that requires the model to answer factoid-style, procedure-style and inconsistent questions about user manuals. |
| Outcome: | The proposed model can answer factoid-style, procedure-style and inconsistent questions about user manuals. |
Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission (2024.findings-naacl)
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| Challenge: | Named entity recognition tasks are often suboptimal for NER . previous work focused on UE-NER, which estimates uncertainty scores for ner . |
| Approach: | They propose to use a Sequential Labeling Posterior Network to estimate uncertainty for NER . they propose to consider wrong-span cases and to evaluate the specificity of wrong-pan cases. |
| Outcome: | The proposed system improves on three datasets and AUPR on MIT-Restaurant datasets. |
Delving into the Openness of CLIP (2023.findings-acl)
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| Challenge: | Contrastive Language-Image Pre-training (CLIP) allows for open-vocabulary visual recognition, where the model can recognize images from an open class set in a zero-shot manner. |
| Approach: | They propose to use image classification as an image-to-text matching task instead of discrete category IDs to achieve open-vocabulary visual recognition. |
| Outcome: | The proposed model can recognize images from an open vocabulary in a zero-shot manner, but its performance deteriorates as the vocabulary expands. |
Towards Effective Automatic Debt Collection with Persona Awareness (2023.emnlp-industry)
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| Challenge: | Existing debt collection agents fail to tailor strategies to debtor personas, leading to ineffective collection. |
| Approach: | They present a commercial practice on debt collection agents that organizes debtor personas into a taxonomy and constructs a persona-aware conversation dataset. |
| Outcome: | The proposed agent increases recovery rate by 3.31% and collects additional 100K RMB after two months of testing. |
LongAlign: A Recipe for Long Context Alignment of Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies to build long context language models focus on context extension and continual training on long text. |
| Approach: | They propose a recipe for instruction fine-tuning on input sequences of similar length . they adopt packing and sorted batching strategies to speed up supervised fine-uning . |
| Outcome: | The proposed model outperforms existing recipes for LLMs in long context tasks by 30% while maintaining proficiency in handling short, generic tasks. |
WantWords: An Open-source Online Reverse Dictionary System (2020.emnlp-demos)
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| Challenge: | Existing reverse dictionary systems only support English reverse dictionary queries . a reverse dictionary can help people who can't remember a word from memory . |
| Approach: | They propose an online reverse dictionary system that outperforms other reverse dictionary systems . it supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries . |
| Outcome: | The proposed reverse dictionary outperforms other reverse dictionary systems on performance . it supports Chinese and English-Chinese as well as Chinese-English queries . |
Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning (2020.findings-emnlp)
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| Challenge: | Existing pretrained language models are agnostic to downstream information and can overfit when fine-tuned with low resource datasets. |
| Approach: | They integrate label information as a task-specific prior into the self-attention component of pretrained BERT models. |
| Outcome: | Experiments on benchmarks and real-word datasets show that the proposed approach can improve the performance of pretrained models when fine-tuned with small datasets. |
BioGraphia: A LLM-Assisted Biological Pathway Graph Annotation Platform (2025.emnlp-demos)
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| Challenge: | Existing methods for obtaining pathway information from biomedical literature rely on simplifying assumptions that limit their ability to capture true complexity of biological reactions. |
| Approach: | They propose a web-based platform to facilitate collaborative pathway graph annotation. |
| Outcome: | The platform supports multi-user collaboration with real-time monitoring, curation, and interactive pathway graph visualization. |
Dependency Parsing via Sequence Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method. |
| Approach: | They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures. |
| Outcome: | The proposed method performs well on DP benchmarks including PTB, UD2.2, SDP15 and SemEval16. |
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
| Challenge: | Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures. |
| Approach: | They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree . |
| Outcome: | The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness. |
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (2022.coling-1)
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| Challenge: | Multi-Document Summarization (MDS) uses the extract-then-abstract paradigm, which extracts a relatively short meta-document and then feeds it into the deep neural networks to generate an abstract. |
| Approach: | They propose to use pre-trained language models to calculate document and keyword’s perplexity to boost other metrics for evaluating a document’s salience. |
| Outcome: | The proposed method can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. |
EventWiki: A Knowledge Base of Major Events (L18-1)
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| Challenge: | Existing knowledge bases focus on static entities such as people, locations and organizations. |
| Approach: | They propose a new knowledge base resource called EventWiki which concentrates on major events . they show that EventWiki is a very useful resource for information extraction regarding events in NLP . |
| Outcome: | The proposed resource is the first knowledge base resource of major events. |
Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model (2024.findings-emnlp)
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| Challenge: | Existing methods to obtain high-quality annotations under limited budgets focus on selecting informative data for expert annotations while the rest of the data is assigned to model annotation. |
| Approach: | They propose a semi-automatic annotation framework that uses error-aware triage and bi-weighting mechanisms to obtain high-quality annotations under limited budget. |
| Outcome: | The proposed framework outperforms baselines in the data annotation problem under limited budgets. |
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2024.acl-long)
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| Challenge: | Existing methods for process-oriented math reward models rely on manual annotation. |
| Approach: | They propose a process-oriented math process reward model called Math-shepherd which assigns a reward score to each step of math problem solutions. |
| Outcome: | The proposed model breaks the bottleneck of manual supervision in two scenarios. |
LegoMT2: Selective Asynchronous Sharded Data Parallel Training for Massive Neural Machine Translation (2025.findings-acl)
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| Challenge: | Existing methods to train a single model for massive languages have huge communication overheads and parameter interference. |
| Approach: | They propose an efficient training approach with an asymmetric multi-way model architecture for massive multilingual neural machine translation. |
| Outcome: | The proposed model is 16.2 faster than the distributed training method for M2M-100-12B while improving the translation performance by an average of 2.2 BLEU on Flores-101. |
ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly used to solve the entity recognition task. |
| Approach: | They propose a framework to select the most informative and representative samples for LLM in-context learning. |
| Outcome: | The proposed framework outperforms baselines on three specialized domain datasets. |
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing methods to classify named entity mentions with fewshots fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario. |
| Approach: | They propose a model that can automatically induce different unde- fined classes from the other class to improve few-shot Named Entity Recognition (NER) . |
| Outcome: | The proposed model outperforms five state-of-the-art models in 1- shot and 5-shots settings on four NER bench marks. |
How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation (2025.findings-acl)
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| Challenge: | Recent studies have focused on dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins. |
| Approach: | They propose to integrate persona metadata into LLMs and use it to iteratively infer contextually appropriate behaviors within dynamic scenarios. |
| Outcome: | The proposed model is based on 15,846 distinct behaviors across 1,001 unique personas and incorporates persona metadata to iteratively infer appropriate behaviors within dynamic scenarios. |
Benchmarking the Fine-Grained Discriminability in Image-Text Retrieval via Controlled Contrastive Differences (2026.findings-acl)
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| Challenge: | Existing cross-modal image-text retrieval models often retrieve samples with inconsistent details. |
| Approach: | They propose two fine-grained image-text retrieval benchmarks that incorporate extensive contrastive samples with one controlled contrastive difference from its anchor. |
| Outcome: | Extensive experiments show that contrastive samples can significantly degrade retrieval performance. |
Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction (2025.emnlp-main)
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| Challenge: | Existing approaches to multimodal relation extraction ignore structural constraints and lack semantic expressiveness for fine-grained relation understanding. |
| Approach: | They propose a framework that reformulates multimodal relation extraction as a retrieval task driven by relation semantics. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability. |
A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization (2021.findings-emnlp)
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| Challenge: | Abstractive summarization models have achieved impressive results on document summarizing tasks, but their performance on dialogue modeling is poor due to the crude and straight methods for dialogue encoding. |
| Approach: | They propose a model that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generate better summaries. |
| Outcome: | The proposed model outperforms various dialogue summarization approaches and achieves state-of-the-art (SOTA) ROUGE results on a SAMsum dataset. |
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)
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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu
| Challenge: | Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering. |
| Approach: | They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow. |
| Outcome: | The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow. |
UNICORN: A Unified Causal Video-Oriented Language-Modeling Framework for Temporal Video-Language Tasks (2024.emnlp-main)
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Yuanhao Xiong, Yixin Nie, Haotian Liu, Boxin Wang, Jun Chen, Rong Jin, Cho-Jui Hsieh, Lorenzo Torresani, Jie Lei
| Challenge: | Recent advances in large multimodal models have encouraged the development of large multi-modal models . however, it is unclear how to extend these models to the more complex video domain . |
| Approach: | They propose a visual instruction tuning framework to address temporal video-language tasks . they collect a dataset and fine-tune the framework on instruction-following data . |
| Outcome: | The proposed model can perform better on established temporal video-language tasks without training objectives and intensive pre-training. |
LLaMA-Berry: Pairwise Optimization for Olympiad-level Mathematical Reasoning via O1-like Monte Carlo Tree Search (2025.naacl-long)
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Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou
| Challenge: | LLaMA-Berry is an advanced mathematical reasoning framework to enhance the problem-solving ability of large language models (LLMs). |
| Approach: | They propose a Monte Carlo Tree Search and Self-Refine framework to optimize reasoning paths and a pairwise reward model to evaluate different paths globally. |
| Outcome: | The proposed framework overcomes inefficiencies and limitations of step-wise and greedy search algorithms, enabling more efficient exploration of solution spaces. |
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation (2024.acl-long)
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Pei Ke, Bosi Wen, Andrew Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Existing models for NLP evaluations lack the ability to generate informative critiques in pointwise grading and pairwise comparison especially without references. |
| Approach: | They propose a method which can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings. |
| Outcome: | The proposed method outperforms all open-source models and even GPT-4 in system-level correlations of pointwise grading. |
ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings (2024.emnlp-main)
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| Challenge: | Attaching suffixes to harmful instructions can hack the defense of Large language models (LLMs) However, due to the unreadable of adversarial suffix, it can be relatively easily penetrated by common defense methods such as perplexity filters. |
| Approach: | They propose an algorithm to embed adversarial suffixes into coherent and understandable text to attack Large language models (LLMs) using a Advbench dataset. |
| Outcome: | The proposed approach reduces the computation time of adversarial suffixes and achieves a much better attack success rate than existing techniques. |
WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem (2026.findings-acl)
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Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Zhu Pengcheng, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie
| Challenge: | despite its linguistic significance, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models. |
| Approach: | They propose to use WenetSpeech-Wu as a large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect of Chinese. |
| Outcome: | The proposed dataset includes 8,000 hours of speech data and strong open-source models . the proposed dataset is competitive and empirically validated . |
Prediction of People’s Emotional Response towards Multi-modal News (2022.aacl-main)
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Ge Gao, Sejin Paik, Carley Reardon, Yanling Zhao, Lei Guo, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya
| Challenge: | BU-NEmo dataset extends from 320 to 1,297 news headline and lead image pairings and collects 38,910 annotations in a crowdsourcing experiment. |
| Approach: | They extend the U.S. gun violence news-to-emotions dataset from 320 to 1,297 news headline and lead image pairings and collect annotations in a crowdsourcing experiment. |
| Outcome: | The proposed models outperform baseline models on the NEmo+ dataset by large margins across several metrics. |
Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition (D18-1)
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| Challenge: | a novel information network decipherment paradigm is proposed for fine-grained coordinated cross-lingual text stream alignment. |
| Approach: | They propose to use Burst Information Networks as media to represent text streams . they propose a simple yet effective information network decipherment algorithm with diverse clues . |
| Outcome: | The proposed approach outperforms existing approaches on bilingual lexicon extraction from coordinated text streams and can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining. |
ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
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| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
| Approach: | They propose a method that leverages layer uncertainty to allocate budget size for each layer to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage of the KV caches to only 20% when compared to full KV inference while achieving nearly lossless performance. |
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)
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Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao
| Challenge: | Expressive zero-shot voice conversion (VC) aims to modify source timbre to match unseen speaker . existing zero- shot VC systems struggle to reproduce paralinguistic information in highly expressive speech . |
| Approach: | They propose a framework for expressive zero-shot voice conversion that uses hybrid content encoding and memory-augmented context-aware timbre modeling. |
| Outcome: | The proposed framework surpasses state-of-the-art VC systems in speech naturalness, speaker similarity, and speaker similarness. |
Croppable Knowledge Graph Embedding (2025.acl-long)
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| Challenge: | Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks. |
| Approach: | They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs. |
| Outcome: | The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity. |
From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)
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Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
| Challenge: | Large language models produce content lacking pedagogical depth when asked to generate lessons . |
| Approach: | They propose a framework that allows teachers to select content according to pedagogical intent and sequence topics so foundations precede applications. |
| Outcome: | The framework achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines. |
InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering (2025.emnlp-main)
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Zihan Wang, Zihan Liang, Zhou Shao, Yufei Ma, Huangyu Dai, Ben Chen, Lingtao Mao, Chenyi Lei, Yuqing Ding, Han Li
| Challenge: | Retrieval-Augmented Generation (RAG) frameworks struggle with identifying whether retrieved documents meaningfully contribute to answer generation. |
| Approach: | They propose a document-related metric to quantify the contribution of retrieved documents to correct answer generation. |
| Outcome: | The proposed framework outperforms existing approaches on both single and multiple retrieval paradigms. |
Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)
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| Challenge: | Current error-handling works are performed in a passive manner, with explicit error- handling instructions. |
| Approach: | They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research. |
| Outcome: | The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances. |
A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement (2026.acl-long)
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Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye
| Challenge: | Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks. |
| Approach: | They propose a Scalable Multi-LLM Collaboration System to coordinate multiple open-source LLMs. |
| Outcome: | The proposed system outperforms prevailing closed-source LLMs on eight mainstream benchmarks on multiple tasks. |
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)
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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li
| Challenge: | Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) . |
| Approach: | They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries. |
| Outcome: | The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. |
MAVEN-FACT: A Large-scale Event Factuality Detection Dataset (2024.findings-emnlp)
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| Challenge: | Event factuality detection is under-explored due to the lack of high-quality large-scale data . efd is a subfield of event understanding, which aims to determine the factuity of textual events. |
| Approach: | They propose a large-scale EFD dataset with factuality annotations of 112,276 events . they find that adopting event arguments and relations helps in event factuity detection . |
| Outcome: | The proposed dataset includes factuality annotations of 112,276 events . it is the largest EFD dataset and is challenging for fine-tuned models and large language models . |
Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image Retrieval (2026.findings-acl)
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| Challenge: | Existing methods to retrieve target images suffer from inherent cognitive bias due to unknown candidate distribution. |
| Approach: | They propose a training-free framework that reframes ZS-CIR as a self-correcting process . they propose to use retrieved results as feedback to perceive the candidate distribution . |
| Outcome: | Experiments on public benchmarks show that CoRR outperforms other SOTA methods. |
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers . fine-timing on resource-constrained edge devices presents significant memory and computational demands . |
| Approach: | They propose a resource-efficient fine-tuning framework for LLMs specifically designed for edge devices. |
| Outcome: | Experiments show that MobiZO achieves substantial runtime speedups and memory savings while improving fine-tuning accuracy. |
SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System (2026.eacl-long)
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| Challenge: | Existing routing methods suffer from poor scalability and dependence on datasets for training . energy footprint is also considered in the decision to implement our new LLM routing framework . |
| Approach: | They propose a new LLM routing framework that dynamically allocates queries to the most appropriate LLM. |
| Outcome: | The proposed method improves data efficiency, adaptability, and routing accuracy compared to existing methods. |
Reinforced Co-Training (N18-1)
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| Challenge: | Existing co-training methods ignore sampling bias and fail to explore the data space. |
| Approach: | They propose a method to select high-quality unlabeled samples to better co-train on by learning a selection policy with a small labeled dataset. |
| Outcome: | The proposed method can obtain more accurate text classification results on clickbait detection and generic text classification tasks. |
Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering (2024.findings-emnlp)
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| Challenge: | Existing research on Event-Event Causal Relation Extraction (ECRE) has highlighted the lack of document-level modeling and causal hallucinations. |
| Approach: | They propose a Knowledge-guided binary Question Answering method with event structures for ECRE that utilizes cross-task knowledge in IE. |
| Outcome: | The proposed method achieves state-of-the-art on the MECI and MAVEN-ERE datasets. |
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models struggle to meet user’s needs when required to generate responses of a specific length due to their inherent difficulty in accurately perceiving numerical constraints. |
| Approach: | They propose a Target Length Generation Task and propose RULER, a model-agnostic approach that controls generated length for large language models. |
| Outcome: | The proposed model-agnostic approach improves instruction-following ability of large language models under length-constrained instructions and can generate appropriate MLT when length constraints are not explicitly provided. |
Extending Context Window of Large Language Models via Semantic Compression (2024.findings-acl)
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| Challenge: | Existing models rely on a quadratic computation to generate long texts . current models impose limitations on the length of text inputs . |
| Approach: | They propose a semantic compression method that extends the context window of large language models . the method reduces the semantic redundancy of long inputs before passing them to the LLMs . |
| Outcome: | The proposed method extends the context window of large language models across tasks . it exhibits consistent fluency in text generation while reducing associated computational overhead. |
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback (2024.findings-naacl)
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Wenda Xu, Daniel Deutsch, Mara Finkelstein, Juraj Juraska, Biao Zhang, Zhongtao Liu, William Yang Wang, Lei Li, Markus Freitag
| Challenge: | Recent large language models (LLMs) are leveraging human feedback to improve their output quality. however, human feedback is costly to collect, especially at inference time when the model provides new, unseen input. |
| Approach: | They propose an inference-time optimization method to refine large language models' output based on fine-grained feedback to pinpoint defects and guide iterative refinement . |
| Outcome: | The proposed method consistently outperforms baseline approaches on three text generation tasks, including machine translation, long-form question answering, and topical summarization. |
Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards (2026.acl-long)
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| Challenge: | Existing methods for reinforcement learning (RL) rely on binary outcome rewards that fail to capture the comprehensiveness and factuality of agents’ reasoning process. |
| Approach: | They propose a reward framework that emphasizes reasoning comprehensiveness, factual grounding, and evidence connectivity. |
| Outcome: | The proposed framework outperforms standard outcome-based RL baselines across multiple deep search benchmarks and shows that it discourages shortcut exploitation and promotes comprehensive, evidence-grounded reasoning. |
HAUNTATTACK: When Attack Follows Reasoning as a Shadow (2026.findings-acl)
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| Challenge: | Emerging Large Reasoning Models (LRMs) excel in mathematical and reasoning tasks, showcasing remarkable capabilities. |
| Approach: | They propose a framework that embeds harmful instructions into reasoning questions . they evaluate 11 LRMs and observe an average attack success rate of over 70% . |
| Outcome: | The proposed framework improves reasoning models by 13 percentage points over baseline. |
TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering (2025.emnlp-main)
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| Challenge: | Existing TableQA benchmarks focus on simple flat tables and suffer from data leakage . current benchmarks are monolingual and fail to capture cross-lingual variability . |
| Approach: | They propose a table-based TableQA benchmark to evaluate LLMs on real-world tasks. |
| Outcome: | The proposed benchmarks show that they achieve high agreement with human judgment . the proposed framework improves on the alignment between model responses and reference answers . |
The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)
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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul
| Challenge: | Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking. |
| Approach: | They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model. |
| Outcome: | The proposed model is more useful than the F-scores reflect and can be used in clinical notes. |
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) due to noisy and fabricating content, it is inevitable that RAG systems are vulnerable to these noises and prone to respond incorrectly. |
| Approach: | They propose to optimize retrieval-augmented generation (RGG) with an Adversarial Tuning Multi-agent system (ATM) ATM steers the Generator to have a robust perspective of useful documents for question answering with the help of an auxiliary Attacker agent. |
| Outcome: | The proposed system improves the retrieval-augmented generator with an auxiliary Attacker agent and can discriminate useful documents amongst fabrications. |
Learning Shared Semantic Space for Speech-to-Text Translation (2021.findings-acl)
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| Challenge: | End-to-end speech translation (ST) has been treated as an independent task . however, the modality gap has rendered MT data and its end-to end models incompatible with their ST counterparts. |
| Approach: | They propose to bridge the representation gap between text and audio inputs by projecting audio and text features to a common semantic representation. |
| Outcome: | The proposed model improves performance on two ST benchmarks and achieves 27.1 BLEU on MuST-C EN-DE. |
CLFFRD: Curriculum Learning and Fine-grained Fusion for Multimodal Rumor Detection (2024.lrec-main)
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| Challenge: | Existing multimodal rumor detection models overlook sample difficulty and order when training . Existing models overlook text-level difficulty, image-level and multimodal difficulty when training samples . |
| Approach: | They propose a curriculum learning framework that uses fine-grained fusion to detect rumors . they propose fusion-based methods that combine text and images to enhance semantic cohesion . |
| Outcome: | The proposed framework outperforms state-of-the-art models on English and Chinese benchmark datasets. |
Modeling Multi-Action Policy for Task-Oriented Dialogues (D19-1)
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| Challenge: | Existing approaches to learn dialogue management only predict one action per turn, limiting expressive power of the conversational agent and introducing unnecessary turns of interactions. |
| Approach: | They propose a model based on a recurrent cell called gated Continue-Act-Slots that overcomes the limitations of existing models and proposes a novel policy model that predicts multiple acts for each turn. |
| Outcome: | The proposed model outperforms existing models on the task of predicting multiple acts for each turn. |
AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction (2023.acl-long)
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| Challenge: | Attribute extraction aims to identify attribute names and the corresponding attribute values from descriptive texts. |
| Approach: | They propose a unified formulation for real-world attribute extraction application, where closed-world, open-world and semi-open attribute extraction tasks are modeled uniformly. |
| Outcome: | The proposed model outperforms existing methods on three datasets and outperformed existing methods by a large margin. |
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs (2025.coling-main)
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| Challenge: | Existing methods to train low-latency multilayer perceptrons (MLPs) on graph tasks are based on graph nodes and lack graph structural information. |
| Approach: | They propose to distill graph structural information from Graph Neural Networks (GNNs) to low-latency multilayer perceptrons (MLPs) on graph tasks. |
| Outcome: | The proposed method does not require graph edges (edge-free setting) yet learns structure-aware MLPs. |
Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)
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| Challenge: | Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective. |
| Approach: | They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism. |
| Outcome: | The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches . |
PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation (2023.acl-long)
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| Challenge: | Image translation is a task that translates an image containing text in the source language to the target language. |
| Approach: | They propose an end-to-end image translation framework that bridges the modality gap between visual inputs and textual inputs/outputs of machine translation (MT). |
| Outcome: | The proposed framework outperforms existing models on a large-scale image translation corpus . it significantly outperformed both cascaded and strong models on the e-commerce domain . |
MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding (2022.acl-long)
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| Challenge: | Existing layout-based pre-training approaches are not easy to apply to VRDU tasks. |
| Approach: | They propose to use markup languages as the backbone for document understanding tasks where text and markup information are jointly pre-trained. |
| Outcome: | The proposed model outperforms existing models on document understanding tasks. |
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation (2024.findings-naacl)
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| Challenge: | Large language models (LLMs) have shown excellent performance on various NLP tasks. |
| Approach: | They propose a method that integrates multiple demonstration users into one aggregated demonstration to improve sequential recommendation. |
| Outcome: | The proposed method outperforms state-of-the-art LLM-based sequential recommendation methods on three recommendation datasets. |
SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models (2025.emnlp-main)
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| Challenge: | Visual Document Retrieval (VDR) relies on text-to-image retrieval using specialized bi-encoders . et al., 2022, 2024, 2021, 2023, 2026, 2030, 2040, 2050, 2060) document retrieval bridges human or artificial agents to the most relevant information, authors say . |
| Approach: | They propose a zero-shot visual document retrieval method that uses bi-encoders . they propose 63.4% nDCG@5 for visual document capture and a reusable semantic proxy . |
| Outcome: | The proposed method surpasses the strongest specialised multi-vector visual document encoder on the ViDoRe-v2 benchmark and scales similarly on MIRACL-VISION with broader multilingual coverage. |
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)
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Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu
| Challenge: | DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks. |
| Approach: | They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks. |
| Outcome: | The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks . |
CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU (2021.emnlp-main)
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| Challenge: | Existing approaches to combine HE and GC in RNNs suffer from long inference latency due to the slow activation functions. |
| Approach: | They propose a hybrid structure of HE and GC gated recurrent unit network, for low-latency secure inferences. |
| Outcome: | The proposed structure improves the secure inference latency by up to 138 over one of the state-of-the-art secure networks on the Penn Treebank dataset. |
Sheaf Discovery with Joint Computation Graph Pruning and Flexible Granularity (2025.emnlp-main)
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| Challenge: | Experimental results show that DiscoGP extracts sheaves that preserve 93-100% of a model’s performance while comprising only 1-7% of the original weights and connections. |
| Approach: | They propose a framework for extracting self-contained modular units within neural language models (LMs) they use a gradient-based pruning algorithm to prune the original LM to a sparse skeleton . |
| Outcome: | The proposed framework preserves 93-100% of the original model's performance while preserving only 1-7% of the model''s original weights and connections. |
CARE: A Clue-guided Assistant for CSRs to Read User Manuals (2024.acl-long)
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| Challenge: | Current solutions don't fit the online custom service scenarios well due to the lack of attention to user questions and possible responses. |
| Approach: | They propose to build a clue-guided assistant for customer service representations (CSRs) that can provide accurate responses and explicitly show explainable paths about how to arrive at these responses. |
| Outcome: | The proposed assistant can reduce CSRs' reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score. |
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Recent studies show impressive results on aspects-based sentiment analysis tasks. |
| Approach: | They analyze the attentions and learned representations of BERT for aspects-based sentiment analysis tasks. |
| Outcome: | The proposed model can be used for aspects-based sentiment analysis (ABSA) but it is not clear how it can provide important features for downstream tasks. |
Learning from Mistakes via Cooperative Study Assistant for Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated their potential to refine their generation based on their own feedback, but the feedback from LLM itself is often inaccurate, thereby limiting its benefits. |
| Approach: | They propose a framework with an auxiliary agent to assist the main LLM in learning from mistakes through interactive cooperation. |
| Outcome: | The proposed framework can significantly boost large language models by an accuracy margin of up to 6.6 on BBH and 12.6 on BBQ. |
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)
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| Challenge: | Existing models for text-to-SQL do not explicitly introduce common knowledge to address comparison relations. |
| Approach: | They propose to leverage adjective-noun phrasing knowledge mined from the web to predict comparison relations in text-to-SQL. |
| Outcome: | The proposed approach improves on the original and re-split Spider datasets on comparison relation prediction. |
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation (2025.findings-emnlp)
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| Challenge: | RAG implementations face challenges in addressing retrieved noise and redundant content . current RAG methods lack the ability to exploit fine-grained inter-document relationships . |
| Approach: | They propose a retrieval-augmented generation framework that exploits latent inter-document relationships while removing irrelevant information and redundant content. |
| Outcome: | The proposed framework achieves consistent performance improvements on knowledge-QA and hallucination-Detection datasets. |
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)
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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
| Challenge: | Existing benchmarks for Complex KBQA lack compositional reasoning capabilities . Existing methods for Complex questions are poor in diversity or scale . |
| Approach: | They propose a compositional programming language to represent the reasoning process of complex questions. |
| Outcome: | The proposed dataset includes around 120K diverse natural language questions . it provides a compositional and interpretable programming language to represent the reasoning process of complex questions based on the proposed model . |
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)
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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions. |
| Approach: | They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level. |
| Outcome: | The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods. |
Learning from Textual Radiology Reports: A Benchmark Dataset for Coronary CT Angiography (2026.acl-industry)
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Sudharshan Balaji, Zhiyu Liu, Zhengyuan Jiang, Shuo Lei, Yimin Chen, Yang Xiao, Shone O. Almeida, Mathew Joseph Karivelil, Christopher Malanga, Ning Wang
| Challenge: | CCTA reports provide an assessment of coronary disease severity to guide patient management. |
| Approach: | They propose a pipeline that decouples structuring from classification by an LLM-based parser . CCTA-RADS is the largest publicly available dataset of CCDA reports . |
| Outcome: | The proposed approach improves the F1-score by 6%-13% compared with direct methods. |
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models often produce inconsistent statements or false facts. |
| Approach: | They propose an efficient weak-supervised adversarial data augmentation approach to generate factual consistency datasets by backpropagating gradients on token embeddings. |
| Outcome: | The proposed model can make interpretable factual errors tracing on public datasets and is cost-effective. |
Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis (2025.acl-long)
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| Challenge: | Recent studies have focused on building dynamic benchmarks to address data contamination issues. |
| Approach: | They propose a method for identifying shortcut neurons through comparative and causal analysis to suppress shortcut neurons. |
| Outcome: | The proposed method overestimates contaminated models and is highly generalizable across benchmarks and hyperparameter settings. |
Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation (2023.emnlp-main)
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| Challenge: | Existing models that use separate recommendation and dialogue modules produce inconsistent results . a multi-task learning model is proposed to bridge the gap between recommendation and generated responses . |
| Approach: | They propose a multi-task learning model that integrates knowledge from two teachers and selectively gates between them via Contextualized Knowledge Distillation. |
| Outcome: | The proposed model significantly improves recommendation performance while enhancing fluency and achieves comparable results in terms of diversity. |
BayesKD: Bayesian Knowledge Distillation for Compact LLMs in Constrained Fine-tuning Scenarios (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized various domains with their remarkable capabilities, but their massive parameter sizes pose significant challenges for fine-tuning and inference. |
| Approach: | They propose a Bayesian Knowledge Distillation framework for compact Large Language Models in resource-constrained fine-tuning scenarios that employs Logits Dual-Scaling, Knowledge Alignment Module, and Bayes Distillations Optimization. |
| Outcome: | The proposed framework outperforms baseline methods on various state-of-the-art LLMs, including LLaMA, Qwen2, Bloom, and Vicuna. |
POS-Constrained Parallel Decoding for Non-autoregressive Generation (2021.acl-long)
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| Challenge: | Existing non-autoregressive generation systems face multimodality problem due to conditionally independent decoding. |
| Approach: | They propose to incorporate linguistic structure into NAG inference instead of teacher AG . they propose a method that provides a specific POS sequence to constrain the NAG model . |
| Outcome: | The proposed method improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. |
KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction (2023.emnlp-main)
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| Challenge: | Existing work on hypernym-hyponym (“is-a”) relations is mostly in the English language. |
| Approach: | They propose a Knowledge Enhanced Prompt Learning method for Chinese hypernym-hyponym relation extraction using Hearst-like patterns as the prior knowledge. |
| Outcome: | The proposed method is able to extract hypernym-hyponym relations from Chinese unstructured texts using Hearst-like patterns and embed patterns and text simultaneously. |
Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)
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| Challenge: | Recent methods to recognize hierarchical discourse relations without explicit connectives are inefficient and ignore the utilization of the output probability distribution information of the verbalizer. |
| Approach: | They propose a global and local hierarchical prompt tuning framework which leverages top-up propagated probability as the global hierarchy to inject it into multi-level verbalizer. |
| Outcome: | The proposed framework achieves competitive results on two benchmacks. |
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation (2022.acl-long)
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| Challenge: | Existing methods to learn speech representations for end-to-end speech-totext translation (ST) neglect the representation discrepancy across modalities. |
| Approach: | They propose a method to calibrate the representation discrepancy between modalities by mixing up the representation sequences of different modality inputs. |
| Outcome: | The proposed method alleviates the cross-modal representation discrepancy and improves on a strong baseline on eight translation directions. |
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation (2026.findings-acl)
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| Challenge: | Existing methods for translating collaborative information into textual prompts or injecting pre-trained embeddings into the LLM treat structural information as static input and fail to capture high-order relational dependencies. |
| Approach: | They propose a framework that generalizes low-rank adaptation from independent to structure-aware propagation by embedding a trainable graph message-passing network within the low-ranked adaptation pathway. |
| Outcome: | Experiments on multiple benchmarks show that GraphLoRA outperforms state-of-the-art recommendation methods and achieves superior generalization. |
Phrase-level Textual Adversarial Attack with Label Preservation (2022.findings-naacl)
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| Challenge: | Existing adversarial attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality. |
| Approach: | They propose a phrase-level perturbation-based adversarial ATtack that generates adversarials through phrase- level perturbations. |
| Outcome: | The proposed approach improves the performance of natural language processing models by reducing the need for word-level perturbations and preserving the fluency and grammaticality of the samples. |
Sentence-level Media Bias Analysis Informed by Discourse Structures (2022.emnlp-main)
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| Challenge: | Recent work on detecting media bias at the level of individual articles is limited to single sentences. |
| Approach: | They propose to use a news discourse structure and PDTB discourse relations to identify bias sentences within an article that can illuminate and explain the overall bias of the entire article. |
| Outcome: | The proposed model can detect bias at the level of individual articles and a single sentence can explain it. |
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference. |
| Approach: | They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning. |
| Outcome: | The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios. |
Extrapolating to Unknown Opinions Using LLMs (2025.coling-main)
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| Challenge: | ice cream flavors and climate change are among the topics people hold on various topics. |
| Approach: | They propose to use a large language model to extrapolate from stances to unknown opinions by prompting and fine-tuning data to improve their ability to extrapole from known to unknown stance. |
| Outcome: | The proposed model can extrapolate from opinions on known topics to unknown ones and generate reasoning behind extrapolation. |
From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)
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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin
| Challenge: | Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency. |
| Approach: | They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process. |
| Outcome: | The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing. |
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning (2024.naacl-long)
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| Challenge: | Prompt tuning on a few data samples presents security issues, e.g., Trojan attacks. |
| Approach: | They propose a method to transfer established data poisoning attacks directly to few-shot prompt tuning, a technique to address the poisoned imbalance issue. |
| Outcome: | The proposed method achieves an ASR of over 99% while maintaining negligible decreases in CDA. |
WACO: Word-Aligned Contrastive Learning for Speech Translation (2023.acl-long)
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| Challenge: | Existing ST methods perform poorly when only a limited amount of parallel data are available for training. |
| Approach: | They propose a Word-Aligned COntrastive learning method for low-resource speech-to-text translation that bridges word-level representations for both speech and text modalities via contrastive learning. |
| Outcome: | The proposed method outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. |
Finding the Pillars of Strength for Multi-Head Attention (2023.acl-long)
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| Challenge: | Recent studies have revealed some issues of Multi-Head Attention (MHA) e.g., redundancy and over-parameterization. |
| Approach: | They propose to train attention heads with a self-supervised group constraint to focus on an essential but distinctive feature subset. |
| Outcome: | The proposed method achieves significant performance gains on three well-established tasks while significantly compressing parameters. |
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning (D19-1)
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| Challenge: | Existing metrics for image captioning evaluation provide an overall quality score, which is difficult to infer specific description errors. |
| Approach: | They propose a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems. |
| Outcome: | The proposed method achieves higher consistency with human judgments and provides more intuitive evaluation results than other metrics. |
Multilingual Translation via Grafting Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to graft pre-trained (masked) language models to multilingual data are limited, and they lack cross-attention component. |
| Approach: | They propose to graft separately pre-trained (masked) language models for machine translation using monolingual data and parallel data. |
| Outcome: | The proposed method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLUE in en2x directions compared with the multilingual Transformer of the same size. |
Evaluation Dataset for Lexical Translation Consistency in Chinese-to-English Document-level Translation (2024.lrec-main)
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| Challenge: | Existing studies on document-level neural machine translation (NMT) assume that all repeated source words should be translated consistently. |
| Approach: | They construct a test set of 310 bilingual news articles to evaluate lexical translation consistency. |
| Outcome: | The proposed test sets show that translation consistency is consistent across multiple languages. |
CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to document-level neural machine translation focus on integrating context into translation, but they focus on the way of integrating contextual information into translation. |
| Approach: | They propose a document-level neural machine translation framework that models cohesion devices from two perspectives: Cohesion Device Masking and Cohetion Attention Focusing. |
| Outcome: | The proposed model outperforms state-of-the-art document-level neural machine translation baselines on three benchmark datasets. |
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
| Approach: | They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages. |
| Outcome: | Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. |
R&R: Metric-guided Adversarial Sentence Generation (2022.findings-aacl)
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| Challenge: | Existing methods prioritize misclassification by maximizing each perturbation’s effectiveness at misleading a text classifier. |
| Approach: | They propose a rewrite and rollback framework for adversarial attack that optimizes a critique score which combines fluency, similarity, and misclassification metrics. |
| Outcome: | The proposed framework outperforms current state-of-the-art in attack success rate by +16.2%, +12.8%, and +14.0% on the classifiers respectively. |
Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios. |
| Approach: | They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character. |
| Outcome: | Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters. |
DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning (2026.findings-acl)
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| Challenge: | Large reasoning models generate long chains of intermediate steps, but their inference cost is dominated by decoding, where each new token must attend to the entire growing sequence. |
| Approach: | They propose a training-free sparse attention mechanism that reduces inference cost by evicting entries from the key-value cache. |
| Outcome: | The proposed model matches or surpasses full attention on reasoning benchmarks . it reduces the number of attended tokens by up to 4.25 and delivers 1.54 speedup . |
Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization (2026.acl-long)
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| Challenge: | Recent studies show that KV cache compression can increase hallucination scores in LLMs . modern LLM models support extremely long sequences, but their impact on model hallucinosity remains underexplored. |
| Approach: | They propose a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context. |
| Outcome: | The proposed method reduces hallucination across multiple models and datasets while preserving computational efficiency. |
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)
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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen
| Challenge: | Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data. |
| Approach: | They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments. |
| Outcome: | The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments. |
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations (2025.emnlp-main)
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Ziwei Ji, Lei Yu, Yeskendir Koishekenov, Yejin Bang, Anthony Hartshorn, Alan Schelten, Cheng Zhang, Pascale Fung, Nicola Cancedda
| Challenge: | LLMs often use assertive language when making false claims, resulting in harm and loss of trust. |
| Approach: | They find that a mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone. |
| Outcome: | a new study shows that mismatch between semantic and verbal uncertainty is better predictor of hallucinations than semantic uncertainty alone. |
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)
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| Challenge: | Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance. |
| Approach: | They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results. |
| Outcome: | The proposed method improves on the datasets from Coursera and XuetangX. |
Adapting a Language Model While Preserving its General Knowledge (2022.emnlp-main)
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| Challenge: | Existing DA-training methods do not explicitly identify what knowledge should be preserved and what should be changed by the domain corpus. |
| Approach: | They propose to use an unlabeled corpus of aparticular domain to train a pre-trained general-purpose language model to adapt the LM so that end-tasks in the domain can give improved performances. |
| Outcome: | The proposed method improves the performance of pre-trained general-purpose language models by contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and specific knowledge. |
MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (2026.acl-long)
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| Challenge: | Multi-turn, long-horizon tasks require dozens of sequential model calls per episode. |
| Approach: | They propose a cost-aware multi-turn LLM routing tool which encodes interaction history and candidate models into joint history–model embeddings and learns an outcome estimator from logged trajectories to predict turn-level model utility. |
| Outcome: | The proposed model reduces cost and performance by 58.7% on ScienceWorld and on Humanity’s Last Exam (HLE) and even reduces costs for held-out tasks. |
VLMInferSlow: Evaluating the Efficiency Robustness of Large Vision-Language Models as a Service (2025.acl-long)
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| Challenge: | Existing studies evaluate efficiency robustness of vision-language models under unrealistic assumptions, requiring access to model architecture and parameters. |
| Approach: | They propose a novel approach to evaluate VLM efficiency robustness in a realistic black-box setting. |
| Outcome: | The proposed approach generates adversarial images with imperceptible perturbations, increasing the computational cost by up to 128.47%. |
Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition (2026.eacl-long)
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| Challenge: | Existing approaches to NLP are static and require manual formalization. |
| Approach: | They propose an adaptive, multi-paradigm, neuro-symbolic inference framework that automatically identifies formal reasoning strategies from problems expressed in natural language and dynamically selects and applies specialized formal logical solvers. |
| Outcome: | The proposed framework outperforms baselines on individual and multi-paradigm reasoning tasks by 17% and 6%. |
Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) data augmentation has attracted increasing attention in recent years due to data sparsity. |
| Approach: | They propose a framework to augment ABSA data using pseudo labels for target domain . they refine generated labeled data using a natural language inference filter . |
| Outcome: | The proposed framework outperforms 7 strong baselines on 4 kinds of ABSA tasks. |
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems (2025.acl-long)
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| Challenge: | Existing reward models focus on human preferences, neglecting verifiable correctness signals. |
| Approach: | They propose a reward system that combines human preference rewards with verifiable correctness signals to provide reliable rewards. |
| Outcome: | The proposed reward agent significantly outperforms vanilla reward models on benchmarks and inference-time best-of-n searches on real-world tasks. |
ImgTrojan: Jailbreaking Vision-Language Models with ONE Image (2025.naacl-long)
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| Challenge: | Existing studies on the safety of large language models (LLMs) with human values have focused on the integration of multi-modal user input into these models. |
| Approach: | They propose a method to bypass safety constraints of large language models by using poisoned images instead of original textual captions. |
| Outcome: | The proposed attack bypasses safety constraints of large language models (VLMs) by replacing the original textual captions with malicious jailbreak prompts. |
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)
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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai
| Challenge: | elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored. |
| Approach: | They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency . |
| Outcome: | The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training. |
Be a Multitude to Itself: A Prompt Evolution Framework for Red Teaming (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have gained increasing attention for their capacity to generate harmful content. |
| Approach: | They propose a scalable evolution framework to evolve red teaming prompts across breadth and depth dimensions, facilitating automatic generation of numerous high-quality and diverse red team prompts. |
| Outcome: | The proposed framework surpasses existing red teaming methods on attack success rate and diversity. |
Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive. |
| Approach: | They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources. |
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)
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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
| Challenge: | a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages. |
| Approach: | They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models. |
| Outcome: | The proposed benchmarks show that the current models perform worse than the human ceiling. |
Sentence-level Media Bias Analysis with Event Relation Graph (2024.naacl-long)
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| Challenge: | Existing studies on media bias at the article level have identified media biases but only a few have been done on article level. |
| Approach: | They propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias identification. |
| Outcome: | The proposed model improves both precision and recall of bias sentence identification. |
Towards Reverse Engineering of Language Models: A Survey (2025.findings-emnlp)
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| Challenge: | Due to the vast amounts of data and computational resources required for model development, protecting the model’s parameters and training data has become an urgent and crucial concern. |
| Approach: | They define "reverse engineering" techniques as attacks on large language models and provide an in-depth analysis of them. |
| Outcome: | The proposed attacks are described as “reverse engineering” techniques on LMs and provide an introduction to existing protective strategies. |
Breaking through Deterministic Barriers: Randomized Pruning Mask Generation and Selection (2023.findings-emnlp)
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| Challenge: | Existing pruning methods focus on a single pruning criterion and lack variety. |
| Approach: | They propose a model pruning strategy that generates several pruning masks randomly and then chooses the optimal mask from the pool of mask candidates. |
| Outcome: | The proposed pruning strategy achieves state-of-the-art performance across eight datasets from GLUE, particularly excelling at high levels of sparsity. |
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)
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Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
| Challenge: | Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event . |
| Approach: | They find that combining lead images and contextual information with text improves news framing . they release the first multimodal news framming dataset related to gun violence in the u.s. |
| Outcome: | The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance . |
Towards More Accurate Uncertainty Estimation In Text Classification (2020.emnlp-main)
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Jianfeng He, Xuchao Zhang, Shuo Lei, Zhiqian Chen, Fanglan Chen, Abdulaziz Alhamadani, Bei Xiao, ChangTien Lu
| Challenge: | Existing models of uncertainty score depend on winning score, which is the maximum probability in a semantic vector. |
| Approach: | They propose to generate accurate uncertainty score by improving the confidence of winning scores by reducing the effect of overconfidence of winning score and considering the impact of different categories simultaneously. |
| Outcome: | The proposed model reduces the effect of overconfidence of winning score and considers impact of different categories of uncertainty simultaneously. |
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis (2025.coling-main)
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| Challenge: | Existing studies have focused on instance-level unlearning, specifically removing predefined instances containing sensitive content. |
| Approach: | They propose a task to erase entity-related knowledge from the target model completely by analyzing the forget set and its size. |
| Outcome: | The proposed task systematically evaluates popular unlearning algorithms and reveals that the knowledge coverage of the forget set and its size play pivotal roles. |
Generative Imagination Elevates Machine Translation (2021.naacl-main)
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| Challenge: | Existing multimodal neural machine translation methods require triplets of bilingual sentence - image for training and tuples of source sentence . Existing methods require truncated images for inference, but ImagiT uses both source sentence and “imagined representation” to produce a target translation. |
| Approach: | They propose a multimodal machine translation method using visual imagination to generate a target translation from a sentence in a source language. |
| Outcome: | The proposed method significantly outperforms the existing text-only neural machine translation baselines and improves translation quality. |
InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model (2025.findings-acl)
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| Challenge: | Existing models for simultaneous speech translation assume pre-segmented speech, limiting their real-world applicability. |
| Approach: | They propose a multi-turn dialogue task that can translate unbounded streaming speech . they construct translation trajectories and robust segments from MuST-C with multi-latency augmentation during training and develop a cache management strategy to facilitate efficient inference. |
| Outcome: | The proposed approach reduces computation-aware latency by 0.5 to 1 second while maintaining the same translation quality compared to baselines. |
MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval (2024.findings-naacl)
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| Challenge: | Large-scale visual-language pretraining models have shown remarkable capabilities in understanding both vision and language. |
| Approach: | They propose a multi-teacher cross-modality alignment distillation technique to integrate the advantages of single-stream and dual-stream models. |
| Outcome: | The proposed model is lightweight and has only 100M running memory and 8.0ms search latency. |
Self-attention-based Graph-of-Thought for Math Problem Solving (2025.findings-acl)
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| Challenge: | Existing methods for generating reasoning paths in a chain structure are inefficient and non-human-like. |
| Approach: | They propose a decoding method for a chain-based LLM that constructs a thought graph simultaneously as an LLM inference and generates reasoning steps with a graph-structured self-attention mechanism. |
| Outcome: | The proposed method improves reasoning accuracy without huge computational over-expensive LLMs and avoids performance degradation issues when the LLM is too small to comprehend complex prompts. |
AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents (2025.acl-demo)
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Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Pang Xin, Zhucong Li, Xu Yinghui
| Challenge: | a new framework automates deployment and debugging of AI projects . complexity of environment configurations, dependency conflicts, and debuggering issues hinder scalability and adoption. |
| Approach: | They propose an end-to-end framework that automates AI project deployment . they conducted experiments on 30 AI deployment cases to evaluate its effectiveness . |
| Outcome: | The proposed framework reduces deployment time and improves success rates by reducing human intervention. |
Few-Shot (Dis)Agreement Identification in Online Discussions with Regularized and Augmented Meta-Learning (2022.findings-emnlp)
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| Challenge: | Existing annotated datasets do not cover all topics of interest. |
| Approach: | They propose a metric-based meta-learning approach that trains a meta-learner with two key abilities: decoding and generalizing domains. |
| Outcome: | The proposed approach can be quickly applied to analyze opinions for new topics with few labeled instances. |
AutoUE: Automated Generation of 3D Games in Unreal Engine via Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) and generative models have motivated studies on automated game generation from natural language descriptions. |
| Approach: | They propose a novel multi-agent system, AutoUE, which coordinates multiple agents to end-to-end generate 3D games, covering model retrieval, scene generation, gameplay and interaction code synthesis, and automated game testing for evaluation. |
| Outcome: | The proposed system covers model retrieval, scene generation, gameplay and interaction code synthesis, and automated game testing for evaluation. |
FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data (2025.naacl-long)
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| Challenge: | Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. |
| Approach: | They propose a method that leverages multi-hop reasoning on context graphs extracted from documents to generate complex multi-level claims without relying on LLMs to decide data labels. |
| Outcome: | The proposed model outperforms GPT-4-o on the LLM-Aggrefact benchmark with much smaller model size. |
Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection (2025.findings-emnlp)
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| Challenge: | Large language models generate coherent and contextually relevant text, but their deployment raises significant concerns about the potential for harmful or inappropriate content. |
| Approach: | They propose a novel inference-time technique that empowers LLMs to self-monitor and correct their outputs dynamically. |
| Outcome: | The proposed method reduces the attack success rate from 77.47% to 5.86%, to Llama-3.1-8B base from 89.70% to 5.56%, and to Qwen2.5-7B-Instruct from 44.44% to 3.84%, without additional training. |
Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024.findings-acl)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong, Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | despite impressive performance, large language models still struggle with hallucinations . current approaches suffer from suboptimal citation quality due to reliance on in-context learning . |
| Approach: | They propose a framework that teaches large language models to generate fine-grained citations. |
| Outcome: | The proposed framework outperforms all baselines on the ALCE benchmark and achieves an average improvement of 14.21% in citation quality. |
At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs (2026.findings-acl)
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| Challenge: | Emotional Quotient (EQ) has emerged as a competency for seamless human-AI integration. |
| Approach: | They propose a framework for a closed-loop EQ evaluation using a PACE taxonomy to define four dimensions of LLM EQ. |
| Outcome: | The proposed framework achieves high alignment of 89.31% with human preferences while maintaining robust consistency of 83.6%. |
Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shifted the post-training paradigm from instruction tuning and human preference alignment to reinforcement learning (RL) based on rule-based evaluations of answer correctness, these models often receive rewards for speculative answers without generating coherent reasoning chains. |
| Approach: | They propose a confidence-based reward model tailored for enhancing STEM reasoning capabilities. |
| Outcome: | The proposed model outperforms state-of-the-art open-source reward models across diverse STEM benchmarks. |
GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering (2025.coling-main)
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| Challenge: | Existing methods for complex table question answering are often implicit, feeding the entire table into prompts. |
| Approach: | They propose a GraphOTTER that explicitly establishes the reasoning process to pinpoint the correct answers. |
| Outcome: | The proposed method is able to identify the correct answers on two benchmark datasets and two LLM backbones. |
On Robustness of Neural Semantic Parsers (2021.eacl-main)
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| Challenge: | Semantic parsing maps natural language (NL) utterances into logical forms (LFs) adversarial examples are created by adding tiny perturbations to inputs but can severely deteriorate model performance. |
| Approach: | They propose to construct robustness test sets based on existing benchmark corpora and to evaluate the effect of data augmentation. |
| Outcome: | The proposed method measures the performance of the proposed parsers on robustness test sets and evaluates the effect of data augmentation. |
Can an Individual Manipulate the Collective Decisions of Multi-Agents? (2025.emnlp-main)
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| Challenge: | Recent studies show that coordinated multi-agent systems exhibit enhanced decision-making and reasoning abilities through collaboration. |
| Approach: | They propose a framework that simulates agent interactions within a multi-agent system to generate adversarial samples and use them to manipulate the target agent in the target system. |
| Outcome: | The proposed framework generates adversarial samples that are used to manipulate the target agent in the target system, misleading the system’s decision-making process. |
Topic-DPR: Topic-based Prompts for Dense Passage Retrieval (2023.findings-emnlp)
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| Challenge: | Prior research focused on optimizing a single prompt as a continuous prompt, but this approach leads to a semantic space collapse, preventing differentiation between relevant and irrelevant passages. |
| Approach: | They propose a dense passage retrieval model that uses topic-based prompts and propose 'positive and negative sampling strategies' to boost dense retrieval efficiency. |
| Outcome: | The proposed model surpasses state-of-the-art retrieval techniques and improves space uniformity. |
Enhancing Reinforcement Learning with Dense Rewards from Language Model Critic (2024.emnlp-main)
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| Challenge: | Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences, but the sparsity of these signals can lead to inefficient and unstable learning. |
| Approach: | They propose a framework that utilizes the critique capability of Large Language Models to produce intermediate-step rewards during RL training. |
| Outcome: | The proposed framework improves sample efficiency and the overall performance of the policy model, supported by both automatic and human evaluation. |
Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)
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Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen
| Challenge: | Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem. |
| Approach: | They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces. |
| Outcome: | The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems. |
Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation (2021.naacl-main)
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| Challenge: | Existing definition generation methods take the source word as an indecomposable semantic unit, but in parataxis languages like Chinese, word meanings can be composed using the word formation process. |
| Approach: | They propose to use word formation features to enhance Definition Generation (DG) in Chinese to generate an explanatory text. |
| Outcome: | The proposed model enhances Definition Generation (DG) in Chinese by decomposing the word meaning into different semantic components. |
Online Iterative Self-Alignment for Radiology Report Generation (2025.acl-long)
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| Challenge: | Existing methods for RRG rely on supervised fine-tuning based on data pairs of radiological images and corresponding radiologist-annotated reports. |
| Approach: | They propose a method that performs supervised fine-tuning on data pairs of radiological images and corresponding radiologist-annotated reports. |
| Outcome: | The proposed method surpasses existing methods and achieves state-of-the-art performance across multiple evaluation metrics. |
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)
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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong
| Challenge: | Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding. |
| Approach: | They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. |
| Outcome: | The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet. |
Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)
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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen
| Challenge: | despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination. |
| Approach: | They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods. |
| Outcome: | The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation . |
CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback (2026.acl-long)
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| Challenge: | Existing methods for generating instruction-code pairs rely on rigid heuristics and are labor-intensive. |
| Approach: | They propose a dual-agent architecture that integrates a Coder and a Reviewer to orchestrate the generation trajectory. |
| Outcome: | The proposed architecture outperforms baselines on a large-scale dataset of instruction-code pairs with stepped difficulty levels. |
A Natural Bias for Language Generation Models (2023.acl-short)
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| Challenge: | a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens. |
| Approach: | They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge. |
| Outcome: | The proposed method improves learning efficiency and improves overall performance. |
Metric Learning for Dynamic Text Classification (D19-61)
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| Challenge: | Traditional text classifiers are limited to predicting over a fixed set of labels, but real-world applications require dynamic classification. |
| Approach: | They propose to replace the traditional fixed-size output layer with a learned metric space . they propose to add or remove support points in the metric and fine-tune the resulting metric . |
| Outcome: | The proposed method is robust to changes in the label space and improves performance in low data regime. |
What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)
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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields. |
| Approach: | They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies. |
| Outcome: | The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges. |
More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection (2026.findings-acl)
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| Challenge: | Existing systems struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities. |
| Approach: | They propose a framework to characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on H-VLI and on established benchmarks. |
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing multilingual machine translation approaches focus on English-centric directions, while non-English directions lag behind. |
| Approach: | They propose a multilingual machine translation system with an emphasis on non-English directions. |
| Outcome: | The proposed model outperforms existing models on English-centric and non-English directions on multilingual translation benchmarks. |
MMD-ERE: Multi-Agent Multi-Sided Debate for Event Relation Extraction (2025.coling-main)
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| Challenge: | Existing research indicates that LLMs can be overconfident and stubborn. |
| Approach: | They propose a multi-agent multi-sided debate approach for event relation extraction which explores the understanding of event relations between different participants before and after the debate. |
| Outcome: | The proposed approach outperforms established baselines on various ERE tasks and LLMs. |
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport (2020.acl-main)
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| Challenge: | Existing models that use only rationales to explain a prediction are limited by the complexity of deep neural networks. |
| Approach: | They extend selective rationalization to text matching by using optimal transport to find a minimal cost alignment between inputs. |
| Outcome: | The proposed model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models. |
Making Pretrained Language Models Good Long-tailed Learners (2022.emnlp-main)
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| Challenge: | Prompt-tuning has shown appealing performance in few-shot classification . however, it is less promising in long-tailed classification due to long tail . |
| Approach: | They propose to use prompt-tuning to make pretrained language models at least good long-tailed learners by bridging the gap between prompt- and commonly used finetun. |
| Outcome: | The proposed method makes pretrained language models at least good long-tailed learners, bridging the gap between prompt-tuning and finetunation. |
Counter-Interference Adapter for Multilingual Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing approaches to multilingual machine translation suffer from performance degradation, resulting in a single model being inferior to separately trained bilingual models on resource-rich languages. |
| Approach: | They propose a transformer-based model with a small parameter overhead for multilingual machine translation that outperforms strong multilingual baselines on 64 of 66 language directions. |
| Outcome: | The proposed model outperforms strong multilingual baselines on 64 of 66 language directions, 42 of which have above 0.5 BLEU improvement. |
A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)
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| Challenge: | Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. |
| Approach: | They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification. |
| Outcome: | The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification. |
GazeVQA: A Video Question Answering Dataset for Multiview Eye-Gaze Task-Oriented Collaborations (2023.emnlp-main)
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| Challenge: | Existing studies on the use of exocentric and egocentric videos in video question answering are focusing on eye-gaze information. |
| Approach: | They propose a task-oriented VQA dataset that captures eye-gaze information . they propose assisting models that ground the perceptual input into semantic information based on three different answer types . |
| Outcome: | The proposed model can ground the perceptual input into semantic information while reducing ambiguities. |
Can Language Models Understand Physical Concepts? (2023.emnlp-main)
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| Challenge: | Existing language models do not understand basic physical concepts in the human world. |
| Approach: | They propose a method to transfer embodied knowledge from visual models to LMs . they use visual concepts and embodies concepts learned from interaction with the world . |
| Outcome: | The proposed method achieves comparable performance with scaling up parameters of LMs 134. |
Task-Oriented Clustering for Dialogues (2021.findings-emnlp)
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| Challenge: | Existing methods for task-oriented dialogue clustering are difficult to apply directly due to inherent differences between them. |
| Approach: | They propose a Dialogue Task Clustering Network model for task-oriented clustering . they use context-aware utterance representations and cross-dialogue utterrance cluster representations . |
| Outcome: | The proposed model outperforms baselines on three public datasets on all metrics. |
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering (2022.findings-acl)
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| Challenge: | Pre-trained language models have shown a great impact on NLP tasks. |
| Approach: | They propose an answer space clustered prompting model and a synonym initialization method that automatically categorizes all answer tokens in a semantic-clustered embedding space. |
| Outcome: | Experiments show that the proposed method outperforms existing state-of-the-art methods in few-shot settings. |
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)
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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang
| Challenge: | Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows . |
| Approach: | They propose a repository-level evaluation benchmark to assess security of AI-generated code. |
| Outcome: | The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation. |
Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)
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| Challenge: | Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness. |
| Approach: | They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL. |
| Outcome: | The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side. |
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)
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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations. |
| Approach: | They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge. |
| Outcome: | The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld. |
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting (2023.findings-acl)
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| Challenge: | Existing approaches to slot filling only learn surface mapping of slot types between D S and D T and get poor generalization capability or robustness. |
| Approach: | They propose a generative zero-shot prompt learning framework for cross-domain slot filling which improves generalization and robustness than previous work. |
| Outcome: | The proposed framework improves generalization and robustness on unseen slots and an efficient prompt tuning strategy boosts performance. |
A Novel Negative Sample Generation Method for Contrastive Learning in Hierarchical Text Classification (2025.coling-main)
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| Challenge: | Existing methods for hierarchical text classification struggle with fine-grained labels, leading to difficulties in accurate classification. |
| Approach: | They propose a hierarchical sequence ranking method for generating diverse negative samples using hierarchically structured hierarchic labels. |
| Outcome: | The proposed method achieves state-of-art (SOTA) on two datasets showing that it can distinguish between fine-grained labels and discriminate. |
RelayAttention for Efficient Large Language Model Serving with Long System Prompts (2024.acl-long)
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| Challenge: | a long system prompt causes throughput/latency bottlenecks as the cost of generating the next token increases w.r.t the sequence length. |
| Approach: | They propose an attention algorithm that reads hidden state from DRAM exactly once for a batch of input tokens. |
| Outcome: | The proposed algorithm reduces the need for redundant memory accesses in existing algorithms. |
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)
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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang
| Challenge: | Current document image parsing solutions rely on specialized models or generate content autoregressively. |
| Approach: | They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation. |
| Outcome: | The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency. |
CoLT5: Faster Long-Range Transformers with Conditional Computation (2023.emnlp-main)
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Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontanon, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai
| Challenge: | Many natural language processing tasks require long inputs, but processing long documents with a Transformer model is expensive due to quadratic attention complexity and applying feedforward and attention projection layers to every input token. |
| Approach: | They propose a long-input Transformer model that builds on the intuition that some tokens are more important than others and uses conditional computation to devote more computation to important tokens. |
| Outcome: | The proposed model achieves stronger performance than LongT5 with faster training and inference, achieving SOTA on the long-input SCROLLS benchmark. |
Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models (2023.emnlp-main)
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| Challenge: | Existing pruning strategies struggle to enhance robustness against adversarial attacks when continually increasing model sparsity and require a retraining process. |
| Approach: | They propose a pruning strategy that replicates embedding space and feature space of dense language models and aims to conserve more pre-trained knowledge during the pruning process. |
| Outcome: | The proposed pruning strategy replicates embedding space and feature space of dense language models, aiming to conserve more pre-trained knowledge during the pruning process. |
Beyond Layout Embedding: Layout Attention with Gaussian Biases for Structured Document Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for encoding layout information rely on millions of learnable parameters . polar coordinates provide superior choice for layout modeling, study finds . |
| Approach: | They propose to model layout attention with Gaussian biases by feeding polar coordinates into 2-D Gausssian kernels. |
| Outcome: | The proposed model improves on three widely used benchmarks. |
Speak No Evil, Just Prompt: Low-resource Multilingual Toxic Speech Detection with Audio Language Model (2026.findings-acl)
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| Challenge: | Existing methods for toxic speech detection rely on high-resource languages and lack acoustic cues. |
| Approach: | They propose a prompt-based adaptation framework that performs end-to-end toxicity detection without ASR. |
| Outcome: | The proposed framework achieves a micro-averaged ROC-AUC of 98.07% on polySpeechTox . it is based on a frozen audio language model and can perform end-to-end toxicity detection without ASR . |
SCOUT: Selective Coupling via Optimal Unbalanced Transport for Interpretable Text Classification (2026.acl-long)
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| Challenge: | Standard interpretable models often rely on scalar similarities that obscure the true evidentiary basis of a prediction. |
| Approach: | They propose a new paradigm that grounds prototype reasoning in the selective correspondence of discriminative fragments. |
| Outcome: | The proposed model outperforms rationale extraction and post-hoc attribution methods on seven benchmarks. |
Polarity Calibration for Opinion Summarization (2024.naacl-long)
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| Challenge: | Existing opinions summarization models emphasize the majority opinions while ignoring the minority opinions. |
| Approach: | They propose a method to align output summary and input text to achieve polarity calibration. |
| Outcome: | The proposed model can mitigate the polarity mismatch between output summary and input text, and maintain the content semantic and language quality. |
Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables (P19-1)
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| Challenge: | Existing work on multi-turn conversations has focused on the relationship between the response and context, but it is lacking a model to model the relationship. |
| Approach: | They propose a conversational semantic relationship RNN model to construct hierarchical dependency between utterances and their context. |
| Outcome: | The proposed model significantly improves the quality of responses in terms of fluency, coherence, and diversity compared to baseline methods. |
DeepPrune: Parallel Scaling without Inter-trace Redundancy (2026.findings-acl)
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| Challenge: | Parallel scaling is a powerful paradigm to enhance reasoning capabilities in large language models. |
| Approach: | They propose a framework that enables efficient parallel scaling through dynamic pruning. |
| Outcome: | The proposed framework achieves token reductions of 65.73% to 88.50% compared to consensus sampling while maintaining competitive accuracy within 3.4 percentage points. |
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin
| Challenge: | Existing frameworks for retrieval-augmented large language models (LLMs) are lacking in LFQA faithfulness testing. |
| Approach: | They propose a framework to teach retrieval-augmented large language models to explicitly discriminate between faithful and unfaithful generations. |
| Outcome: | The proposed framework outperforms GPT-4o in LFQA scenarios and outperformed existing benchmarks. |
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)
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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen
| Challenge: | Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance. |
| Approach: | They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents. |
| Outcome: | The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain. |
How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation (2026.findings-acl)
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| Challenge: | Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. |
| Approach: | They reversely traced information flow across decoding, projection, and activation phases and found that CoT may serve as a decoding space pruner . |
| Outcome: | The proposed framework can be used to design more efficient and robust prompts. |
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models (2024.acl-long)
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| Challenge: | In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the results are reliable. |
| Approach: | They propose a framework for human evaluation of generative large language models that takes into account usability, aesthetics and cognitive biases. |
| Outcome: | The proposed framework is based on the framework proposed by Deutsch and alnajjar . it is aimed at ensuring that human evaluation is accurate in the age of generative AI . |
A Neural Network Based Model for Loanword Identification in Uyghur (L18-1)
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| Challenge: | Lexical borrowing happens in almost all languages, and we propose a new method to identify loanwords in Uyghur. |
| Approach: | They propose a neural network based loanword identification model for Uyghur that captures past and future information and learns both word level and character level features automatically. |
| Outcome: | The proposed model outperforms baseline models on Chinese, Arabic and Russian loanword detection in Uyghur. |
Compartmentalised Agentic Reasoning for Clinical NLI (2026.findings-acl)
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| Challenge: | Large language models produce fluent judgments for clinical natural language inference, yet fail when the decision requires the correct inferential schema rather than surface matching. |
| Approach: | They propose a compartmentalised agentic framework that routes each premise–statement pair to a reasoning family and applies a specialised solver with explicit verification and targeted refinement. |
| Outcome: | The proposed framework improves mean accuracy from 23% with direct prompting to 57%, with the largest gains on structurally demanding reasoning types. |
Dynamic Knowledge Distillation for Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Existing methods conduct knowledge distillation statically, e.g., student model aligns output distribution to teacher model on pre-defined training dataset. |
| Approach: | They propose a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency . they find it is promising and provide discussions on potential future directions towards more efficient methods . |
| Outcome: | The proposed method can boost student model performance while accelerating training . the proposed method reduces memory usage and accelerates model inference . |
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies have explained to what extent LLMs extract conflicting knowledge from the provided text, but they neglect the necessity to reason with conflicting information. |
| Approach: | They construct a dataset for knowledge conflict resolution examination in the form of question answering that divides reasoning with conflicting knowledge into three levels. |
| Outcome: | The proposed dataset enables analysis of reasoning with conflicting knowledge in the form of question answering. |
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)
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| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
| Approach: | They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system. |
| Outcome: | The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance. |
Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)
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| Challenge: | Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics. |
| Approach: | They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions. |
| Outcome: | The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results. |
Learn to Not Link: Exploring NIL Prediction in Entity Linking (2023.findings-acl)
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| Challenge: | Entity linking models have been successful in capturing semantic features, but the NIL prediction problem has not been addressed. |
| Approach: | They propose an entity linking dataset that categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrases. |
| Outcome: | The proposed dataset categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrase categories and ensures the presence of mentions by human annotation and entity masking. |
KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)
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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu
| Challenge: | Recent advances in pre-trained language models have made it possible to generate human-like text. |
| Approach: | They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached. |
| Outcome: | The proposed game lacks incentives and relies on players to explore on their own. |
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations, but they are plagued by the Knowledge Hallucination problem. |
| Approach: | They propose a method that exploits the dialogue-knowledge interaction to reduce hallucination by using external knowledge resources to generate more informative responses. |
| Outcome: | The proposed method reduces hallucination without disrupting other dialogue performance while keeping adaptive to different generation models. |
DiscoverGPT: Multi-task Fine-tuning Large Language Model for Related Table Discovery (2025.findings-naacl)
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Xuming Hu, Xiao Qin, Chuan Lei, Asterios Katsifodimos, Zhengyuan Shen, Balasubramaniam Srinivasan, Huzefa Rangwala
| Challenge: | Existing methods to learn and evaluate the table semantic relatedness of tabular data are based on pretrain-and-finetune paradigms. |
| Approach: | They propose a multi-task fine-tuning framework that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task. |
| Outcome: | The proposed framework outperforms the best performing baseline by up to 7% in F1 score. |
BPO: Staying Close to the Behavior LLM Creates Better Online LLM Alignment (2024.emnlp-main)
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| Challenge: | Existing offline DAP methods for aligning large language models with human preference are computationally expensive due to their two-stage training pipeline that consists of a reward modeling phase. |
| Approach: | They propose to align large language models to human desiderata from offline preference datasets by using an online approach. |
| Outcome: | The proposed approach improves performance across a wide range of tasks when training with the same amount of preference data. |
Unsupervised Recurrent Neural Network Grammars (N19-1)
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| Challenge: | RNNGs model syntax and structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. |
| Approach: | They explore unsupervised learning of recurrent neural network grammars for language modeling and grammar induction. |
| Outcome: | The proposed model outperforms standard sequential language models and improves parsing performance. |
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade (2021.findings-emnlp)
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| Challenge: | Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks. |
| Approach: | They propose a framework which emits predictions in internal layers without passing through the entire model. |
| Outcome: | The proposed framework can achieve 15% improvement under 4x speed-up compared with existing methods on six classification tasks yielding more calibrated and accurate predictions. |
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization (2023.findings-emnlp)
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| Challenge: | Recent studies show that sequence-to-sequence (seq2sequ) models struggle with compositional generalization (CG) a crucial property of human language learning is its compositional globalization (GC), the algebraic ability to understand and produce a potentially infinite number of novel combinations from known components. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) extension which learns to compose representations of different encoder layers dynamically for different tasks. |
| Outcome: | The proposed model achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of the proposed model. |
Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens (2024.findings-emnlp)
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| Challenge: | Existing studies have explored compression and accumulation methods to compress contexts, but these methods lose useful context information during the compression process, leading to performance degradation. |
| Approach: | They propose a method that allows LLMs to take a deep breath and insert a special token at the end of each chunk. |
| Outcome: | Experiments on language modeling and out-of-domain tasks validate the superiority of the proposed method. |
Learning Kernel-Smoothed Machine Translation with Retrieved Examples (2021.emnlp-main)
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| Challenge: | Existing methods to update deployed models are prone to overfit . however, non-parametric methods are liable to over-fit the retrieved examples . |
| Approach: | They propose to learn Kernel-Smoothed Translation with Example Retrieval (KSTER) this approach allows users to adapt models to emerging cases without retraining . |
| Outcome: | The proposed approach achieves 1.1 to 1.5 BLEU scores over existing methods without retraining . the proposed model is released on https://github.com/jiangqn/KSTER. |
Combating Security and Privacy Issues in the Era of Large Language Models (2024.naacl-tutorials)
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| Challenge: | a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs . |
| Approach: | This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs . |
| Outcome: | This tutorial aims to provide a systematic summary of risks and vulnerabilities in large language models . it will also outline emerging challenges in security, privacy and reliability of LLMs . |
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval (2025.acl-long)
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| Challenge: | Existing methods for Knowledge-Based Visual Question Answering lack multimodal retrieval . large language models (LLMs) have demonstrated remarkable generalization and reasoning capabilities in text-based systems. |
| Approach: | They propose a multimodal vision-language retrieval-augmented generation system that harmonizes multiple modalities and modality to enhance retrieval. |
| Outcome: | The proposed system achieves state-of-the-art retrieval performance and competitive answers on InfoSeek and Encyclopedic-VQA benchmarks. |
Proofread: Fixes All Errors with One Tap (2024.acl-demos)
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Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai
| Challenge: | Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio. |
| Approach: | They propose a two-stage tuning approach to acquire the dedicated Large Language Model for the feature, followed by a reinforcement learning approach for targeted refinement. |
| Outcome: | The proposed model achieves 85.56% good quality on Rewrite and proofread tasks on human-labeled golden sets. |
Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (2023.findings-acl)
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| Challenge: | Existing methods to improve persona consistency on high-quality human-labeled persona datasets face high cost and poor scalability. |
| Approach: | They propose a method to improve zero-shot persona consistency via in-context learning by pre-training a persona-augmented dialogue generation model and then using in-constant prompting mechanism to realize zero- shot persona customization. |
| Outcome: | The proposed method improves persona consistency without compromising coherence and informativeness in zero-shot settings. |
PaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers. |
| Approach: | They propose a multi-modal multi-document benchmark for agentic deep research that integrates evidence from multiple documents. |
| Outcome: | Experimental results show that even advanced systems achieve limited scores on PaperScope . paper provides a rigorous benchmark alongside a pipeline for constructing large multi-modal, multi-source deep research datasets. |
Discovering a Shared Logical Subspace: Steering LLM Logical Reasoning via Alignment of Natural-Language and Symbolic Views (2026.acl-long)
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| Challenge: | Existing approaches to multistep logical reasoning are limited by natural language refinement or external symbolic solvers. |
| Approach: | They propose a logical subspace that captures logical reasoning capabilities in LLMs that are shared across views while remaining independent of surface forms. |
| Outcome: | The proposed approach improves accuracy by 11 percentage points and generalizes well on out-of-domain problems. |
CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
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| Challenge: | Existing approaches to knowledge-grounded dialogue generation perform relatively independent sub-tasks . Typical approaches tend to decompose this task into two streamlined sub- tasks . |
| Approach: | They propose a collaborative latent variable model to integrate knowledge selection and knowledge-aware response generation simultaneously in separate but collaborative latences. |
| Outcome: | The proposed model outperforms previous methods on knowledge selection and response generation. |
LEGENT: Open Platform for Embodied Agents (2024.acl-demos)
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Zhili Cheng, Zhitong Wang, Jinyi Hu, Shengding Hu, An Liu, Yuge Tu, Pengkai Li, Lei Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing integrations of large language models and large multimodal models are limited . Existing platforms for developing embodied agents are limited and limited based on open-source software. |
| Approach: | They propose an open platform for developing embodied agents using LLMs and LMMs. |
| Outcome: | The proposed platform surpasses GPT-4V in embodied tasks with its model training on LEGENT data. |