Papers by Lei Wang
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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%. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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 . |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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%. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |