Papers by Lei Liu
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for numerical reasoning are not flexible enough to handle diverse expressions. |
| Approach: | They propose a Relational Graph enhanced Hybrid table-text Numerical reasoning model with Tree decoder which captures relationship between numerical value, table schema, and text information on the encoder side. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on the publicly available tabletext hybrid QA benchmark. |
TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions, but they are limited by noise and e.g., users may click questions they don't like, leading to inaccurate semantics modeling. |
| Approach: | They propose to introduce tags of FAQ questions to reduce noise in the conversation context and integrate them into a reinforcement learning framework to minimize the negative impact of irrelevant information. |
| Outcome: | The proposed method can eliminate irrelevant information and minimize negative impact of irrelevant information in the dynamic conversation context. |
A Dialogue-based Information Extraction System for Medical Insurance Assessment (2021.findings-acl)
Copied to clipboard
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. |
Too Long, Do Re-weighting for Efficient LLM Reasoning Compression (2026.acl-long)
Copied to clipboard
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. |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing unsupervised methods for paraphrase generation are weak in semantic equivalence or expression diversity. |
| Approach: | They propose a framework for unsupervised paraphrase generation that employs multi-aspect equivalence constraints and multi-granularity diversifying mechanisms to achieve good semantic equvalence and expressive diversity. |
| Outcome: | The proposed framework achieves 9.1% and 3.3% absolute gains over previous SOTA on Quora and MSCOCO and can improve to 18.0% and 4.6% on GLUE. |
MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)
Copied to clipboard
Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu
| Challenge: | Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning. |
| Approach: | They propose a multimodal scientific dataset and benchmark curated from open-access publications. |
| Outcome: | MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers. |
Fantastic Expressions and Where to Find Them: Chinese Simile Generation with Multiple Constraints (2023.acl-long)
Copied to clipboard
| Challenge: | Existing attempts to generate similes as context-free tasks are not suitable for simile generation . however, simile generated under such settings might be undesirable, we argue . |
| Approach: | They propose a model to generate a simile with multiple simile elements . they propose to use a vehicle retrieval module to obtain the explicable comparison . |
| Outcome: | The proposed model can generate a simile with multiple simile elements, e.g., context and vehicle. |
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)
Copied to clipboard
| Challenge: | Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge. |
| Approach: | They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms. |
| Outcome: | The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
Copied to clipboard
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. |
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (D18-1)
Copied to clipboard
| Challenge: | Existing methods for learning word and entity representations in monolingual settings are limited. |
| Approach: | They propose a method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge and enables cross-linguistic inferences. |
| Outcome: | The proposed method captures mutually complementary knowledge and enables cross-lingual inferences among knowledge bases and texts. |
Large Language Models are not Fair Evaluators (2024.acl-long)
Copied to clipboard
Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, Zhifang Sui
| Challenge: | Existing evaluation frameworks that use large language models as referees are insufficient for accurately assessing their alignment with human intent. |
| Approach: | They propose a calibration framework to address positional bias in large language models as evaluators by manually annotating the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicun A Benchmark’s question prompt. |
| Outcome: | The proposed framework alleviates evaluation bias, resulting in closer alignment with human judgments. |
DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections (2026.findings-acl)
Copied to clipboard
| Challenge: | Several studies rely on additional models to optimize mixtures. |
| Approach: | They propose a method that dynamically optimizes instruction-tuning dataset mixtures by prior-scaled Boltzmann Exploration and a multi-armed bandit setup. |
| Outcome: | The proposed method improves the TÜLU-2-mixture and TÜLO-3-mixtures across 10 benchmarks while introducing minimal computational overhead over naive sampling. |
RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current temporal knowledge graph question answering methods focus on implicit temporal constraints and lack the capability to handle complex temporal queries. |
| Approach: | They propose a temporal knowledge graph question answering framework that recursively decomposes questions into sub-problems and employs multi-path answer aggregation to improve fault tolerance. |
| Outcome: | The proposed framework outperforms existing methods on multiTQ and TimelineKGQA benchmarks. |
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA (2025.findings-acl)
Copied to clipboard
Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li
| Challenge: | Current long-context large language models lack citations to support their responses, making verification difficult due to potential hallucinations. |
| Approach: | They propose to use off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations and leverage this pipeline to construct a large-scale SFT dataset for LQAC. |
| Outcome: | The proposed pipeline can generate responses with fine-grained citations on the fly, surpassing existing models including GPT-4o. |
Stimulate the Critical Thinking of LLMs via Debiasing Discussion (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies show that large language models (LLMs) are often prone to stance homogeneity and human preference biases when faced with conflicting perspectives. |
| Approach: | They propose a novel two-stage training framework to address stance homogeneity bias and human preference bias by generating multi-model discussion datasets and optimizing reinforcement learning from human feedback to align with discussion correctness. |
| Outcome: | The proposed framework reduces stance homogeneity bias and human preference bias and improves generalization capabilities on non-discussion scenarios and out-of-domain datasets. |
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction (2026.findings-acl)
Copied to clipboard
| Challenge: | Muon’s orthogonalized update rule suppresses the emergence of heavy-tailed weight spectra and over-emphasizes the training along noise-dominated directions. |
| Approach: | They propose to preserve Muon's ability to capture parameter interdependencies while producing heavier-tailed updates and inducing heavier-tail weight spectra. |
| Outcome: | The proposed algorithm suppresses the emergence of heavy-tailed weight spectra and over-emphasizes training along noise-dominated directions. |
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown nearly saturated performance on many NLP tasks. |
| Approach: | They construct multiple sensitive factors time QA which encompasses three temporal factors . they test current mainstream LLMs with different parameter sizes . |
| Outcome: | The proposed model incorporates three temporal factors with 2,853 samples . the results show that LLMs fall behind smaller models on these factors . |
Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)
Copied to clipboard
null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei
| Challenge: | Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts. |
| Approach: | They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis. |
| Outcome: | The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents. |
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)
Copied to clipboard
| Challenge: | Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations . |
| Approach: | They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios. |
| Outcome: | The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL. |
HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for enhancing sequential recommendation use long interaction sequences, but they lack the ability to extract user preferences from long sequences. |
| Approach: | They propose a plugin that integrates LLMs to infer user preferences from interaction sequences. |
| Outcome: | The proposed algorithms improve user semantic embedding extraction and utilization on three benchmark datasets. |
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)
Copied to clipboard
| 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. |
DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)
Copied to clipboard
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)
Copied to clipboard
| 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. |
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK. |
| Approach: | They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms. |
| Outcome: | The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units . |
Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)
Copied to clipboard
Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang
| Challenge: | Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability. |
| Approach: | They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action . |
| Outcome: | The proposed agent improves 19.8% over baselines on complex questions and multi-tasks. |
LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN (2026.findings-acl)
Copied to clipboard
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. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
Copied to clipboard
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. |
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
| Outcome: | The proposed approach can be simplified to generate recommendations from the entire pool of items. |
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning. |
| Approach: | They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds. |
| Outcome: | The proposed task solves the problems in an aspect-based sentiment analysis task. |
Evaluating Generative Language Models in Information Extraction as Subjective Question Correction (2024.lrec-main)
Copied to clipboard
| Challenge: | Modern large language models (LLMs) perform poorly in elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation methods. |
| Approach: | They propose a method to evaluate large language models by incorporating a human annotation schema. |
| Outcome: | The proposed evaluation method improves matching between model outputs and golden labels. |
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)
Copied to clipboard
| 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. |
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models (2026.findings-acl)
Copied to clipboard
Hoang Phan, Xianjun Yang, Yuanshun Yao, Jingyu Zhang, Shengjie Bi, Xiaocheng Tang, Madian Khabsa, Lijuan Liu, Deren Lei
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning . however, the recipe introduces a significant risk of capability regression, where models forget foundational skills after prolonged training without employing regularization strategies. |
| Approach: | They propose a replay strategy with dynamic objective reweighting for general knowledge preservation using short-horizon signals of convergence and instability. |
| Outcome: | The proposed method preserves general capabilities and improves reasoning . it can be applied to existing RLVR pipelines without training additional models or tuning . |
Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)
Copied to clipboard
| Challenge: | Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult. |
| Approach: | They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB. |
| Outcome: | The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP. |
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)
Copied to clipboard
| 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. |
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge (P19-1)
Copied to clipboard
| Challenge: | Existing methods for topic-to-essay generation are insufficient for generating novel, diverse, and topic-consistent paragraph-level text with a set of topics. |
| Approach: | They propose to integrate commonsense from external knowledge base into the generator through dynamic memory mechanism and adversarial training to further improve topic-consistency. |
| Outcome: | The proposed task is more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation. |
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for addressing ambiguities in conversational search systems are one-size-fits-all and struggle to achieve effective domain transferability. |
| Approach: | They propose a method to provide search engines with strategies regarding when to ask clarification questions in a post-hoc manner. |
| Outcome: | The proposed method improves search performance 10% on four unseen domains. |
R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms (2026.acl-industry)
Copied to clipboard
| Challenge: | Existing approaches to query–document relevance assessment are limited . ambiguous user intent and asymmetric relevance are challenges for RAG platforms . |
| Approach: | They propose a decomposed reasoning model for relevance assessment that decomposes query intent into intent inference and evidence grounding. |
| Outcome: | The proposed model outperforms strong baselines on offline benchmarks and achieves significant gains in large-scale online A/B testing. |
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (2023.emnlp-main)
Copied to clipboard
| 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. |
Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for reweighting data mixtures rely on manual designation with certain heuristics based on intuition or empirical results. |
| Approach: | They propose a model-based framework that learns to re-weight domains by reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment. |
| Outcome: | The proposed framework outperforms baselines in achieving balanced performance across source and target fields and domain spaces without retraining. |
BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook (2026.acl-long)
Copied to clipboard
Hao Gu, Lujun Li, Hao Wang, Lei Wang, Zheyu Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Sirui Han, Yike Guo
| Challenge: | Recent sparsity-aware binarization approaches can achieve sub-1-bit compression, but they face performance degradation, mask-management overhead, and limited hardware compatibility. |
| Approach: | They propose a binary quantization framework that leverages binary pattern clustering and weight transformation to overcome performance degradation and mask-management overhead. |
| Outcome: | The proposed framework achieves state-of-the-art compression (1.11–0.7 bits) it maintains high performance with only a 3.1% accuracy drop in zero-shot benchmarks while delivering a 1.6 speedup over FP16. |
Collaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completion (2026.acl-long)
Copied to clipboard
| Challenge: | Existing multi-modal knowledge graphs lack modality-specific information and are limited in their ability to capture nuanced semantic interplay between modalities. |
| Approach: | They propose a multi-modal knowledge graph completion method which integrates both paradigms . they use a fine-grained Entity Representation Factorization module and a Robust Relation-aware Modality Fusion module to obtain robust representations for three independent modalities and one fused modality. |
| Outcome: | The proposed method achieves coexistence and collaboration of fused and independent modality representations while maintaining modality-specific information. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
Copied to clipboard
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. |
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models (2026.findings-acl)
Copied to clipboard
Kailin Jiang, Ning Jiang, Yuntao Du, Yuchen Ren, Yuchen Li, Yifan Gao, Jinhe Bi, Yunpu Ma, Bin Li, Lei Liu, Qing Li
| Challenge: | Existing benchmarks for Large Multimodal Models (LMMs) are constrained by static representations, inadequately evaluating their ability to understand time-sensitive knowledge. |
| Approach: | They propose a benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types to evaluate temporal awareness along 6 key dimensions and 11 challenging tasks. |
| Outcome: | The proposed benchmark measures temporal awareness along 6 key dimensions and 11 tasks, while most open-source LMMs still lack time understanding ability. |
Bayesian Optimization for Controlled Image Editing via LLMs (2025.findings-acl)
Copied to clipboard
Chengkun Cai, Haoliang Liu, Xu Zhao, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, John Lee, Jenq-Neng Hwang, Lei Li
| Challenge: | achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularly concerning grounding techniques and the necessity for model fine-tuning. |
| Approach: | They propose an off-the-shelf approach that integrates Large Language Models with Bayesian Optimization to facilitate precise and user-friendly image editing. |
| Outcome: | The proposed approach outperforms existing methods in editing accuracy and semantic preservation, as validated using different LLMs including Claude3 and GPT-4. |
Controllable Text Generation with Focused Variation (2020.findings-emnlp)
Copied to clipboard
Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, Piero Molino
| Challenge: | Focused-Variation Network (FVN) is a new model to control language generation. |
| Approach: | They propose a model that learns discrete latent spaces for each attribute inside codebooks and uses them to generate fluent text. |
| Outcome: | The proposed model can generate fluent and mostly coherent text on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations. |
GEM: A General Evaluation Benchmark for Multimodal Tasks (2021.findings-acl)
Copied to clipboard
Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
| Challenge: | Existing datasets that focus on natural language tasks are not considered as a general evaluation benchmark for multimodal tasks. |
| Approach: | They present a general evaluation benchmark for multimodal tasks, GEM 1 . they compare it with existing multimodal vision-language datasets . |
| Outcome: | The proposed model is compared with existing vision-language datasets focusing on natural language tasks . it is the largest vision-linguistic dataset covering image-language tasks and video-language task at the same time . |
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning (2026.acl-long)
Copied to clipboard
Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Fei Huang, Ya-Qin Zhang, Yang Liu
| Challenge: | Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited. |
| Approach: | They propose an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT. |
| Outcome: | Experiments on 7B-scale writer models show that Writing-RL improves long-form writing performance over strong SFT baselines. |
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm (2022.findings-naacl)
Copied to clipboard
| Challenge: | Prompt-based learning inherits the vulnerability from pre-training, where model predictions can be misled by inserting triggers into the text. |
| Approach: | They propose a potential solution to mitigate this vulnerability by injecting triggers into pre-trained language models using only plain text. |
| Outcome: | The proposed learning paradigm inherits the vulnerability from the pre-training stage . it can totally control or severely decrease the performance of prompt-based models . |
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting (2026.acl-industry)
Copied to clipboard
| 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)
Copied to clipboard
| 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 Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)
Copied to clipboard
| Challenge: | Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. |
| Approach: | They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages. |
| Outcome: | The proposed framework improves on four low-resource agglutinative language tasks. |
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment (2024.emnlp-main)
Copied to clipboard
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. |
LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)
Copied to clipboard
| Challenge: | prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios. |
| Approach: | They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework. |
| Outcome: | The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data. |
GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art (2025.acl-long)
Copied to clipboard
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. |
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. |
| Approach: | They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data. |
| Outcome: | The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification. |
The Role of Deductive and Inductive Reasoning in Large Language Models (2025.acl-long)
Copied to clipboard
Chengkun Cai, Xu Zhao, Haoliang Liu, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Lei Li
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning tasks, yet their reliability in problem-solving remains debatable. |
| Approach: | They propose a framework that integrates both deductive and inductive reasoning approaches to enhance LLM reasoning by progressively adapting its reasoning pathways based on problem complexity. |
| Outcome: | The proposed framework achieves 70.3% accuracy on AIW, compared to 62.2% for Tree of Thought, while maintaining lower computational costs. |
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications. |
| Approach: | They propose a prototype-based emotion transfer framework that can be used in real-world applications. |
| Outcome: | The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation. |
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)
Copied to clipboard
Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng
| Challenge: | Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting. |
| Approach: | They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance. |
| Outcome: | The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity. |
ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)
Copied to clipboard
Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen
| Challenge: | drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers . |
| Approach: | They propose a framework that automates method statement generation by using multi-agent collaboration. |
| Outcome: | The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. |
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming general-purpose APIs, requiring visual knowledge to be understood. |
| Approach: | They propose to evaluate the visual capability of large-scale large-language models through visual commonsense evaluation using a human-annotated dataset. |
| Outcome: | The proposed dataset compares the visual commonsense knowledge of large-scale models with those of unimodal LLMs and visually augmented models. |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
Copied to clipboard
Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. |
| Approach: | They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite . |
| Outcome: | The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query . |
Continual Training of Language Models for Few-Shot Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. |
| Approach: | They propose to continuously post-train an LM with unlabeled domains to expand its knowledge without forgetting previous skills. |
| Outcome: | The proposed system improves few-shot end-task learning in these domains. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
Copied to clipboard
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. |
Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)
Copied to clipboard
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. |
Know the Known and the Unknown: Reasonable Answer Generation with Knowledge-Informed Citations (2026.acl-long)
Copied to clipboard
Yichi Zhang, Zhuo Chen, Lingbing Guo, Jun Xu, Mengshu Sun, Zhizhen Liu, Lei Liang, Wen Zhang, Huajun Chen
| Challenge: | Existing approaches focus on generating multi-level citations linked to specific references, making it verifiable and trustworthy. |
| Approach: | They propose a new data construction pipeline and a benchmark to improve citation granularity and awareness of unknown information. |
| Outcome: | The proposed model improves on the existing benchmark and data construction pipeline and provides citation granularity and awareness of unknown information. |
RACQC: Advanced Retrieval-Augmented Generation for Chinese Query Correction (2025.findings-emnlp)
Copied to clipboard
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. |
MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have limitations in grounding ideas and mitigating confirmation bias during refinement. |
| Approach: | They propose a framework that integrates a Motivational Knowledge Graph with a Q-Driven Socratic Ideator to enhance LLM ideation. |
| Outcome: | The proposed framework enhances LLM ideation by integrating a Motivational Knowledge Graph with a Q-Driven Socratic Ideator. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
Copied to clipboard
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)
Copied to clipboard
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. |
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)
Copied to clipboard
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. |
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing benchmarks fail to evaluate extremely long-context LLMs or analyze their limitations. |
| Approach: | They propose a Synthetic, Scalable, Systematic evaluation suite for LLMs using SQL execution. |
| Outcome: | The proposed evaluation suite is able to scale text length and difficulty across scenarios and provides strong correlations with real-world benchmarks. |
DCP: Dual-Cue Pruning for Efficient Large Vision-Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing pruning methods for large vision language models use visual tokens to prune . existing methods fail to balance efficiency and semantic alignment due to large number of visual token. |
| Approach: | They propose a cross-modal pruning framework that considers textual semantics and visual self-attention to combine them to achieve efficient inference acceleration. |
| Outcome: | The proposed pruning framework can retain only 25% of the visual tokens, with a minimal performance degradation of only 0.063% on LLaVA-1.5-13B. |
Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)
Copied to clipboard
| Challenge: | Text-based knowledge graph completion methods neglect knowledge contexts in inferring process. |
| Approach: | They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. |
| Outcome: | The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets. |
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback (2026.findings-acl)
Copied to clipboard
Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen
| Challenge: | Time series anomaly detection (TSAD) has traditionally focused on binary classification and lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. |
| Approach: | They propose a time-series reasoning task that reformulates TSAD from discriminative to reasoning-intensive paradigm. |
| Outcome: | The proposed task reformulates TSAD from discriminative to reasoning-intensive paradigm. |
KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks for deep text understanding have encountered two major limitations . most require human annotation of knowledge, which leads to limited knowledge coverage . |
| Approach: | They propose a benchmark to help readers understand a document with prior knowledge . they use massive knowledge bases to guide annotators and large language models to construct knowledgable questions . |
| Outcome: | The proposed benchmarks have limited knowledge coverage and use choices or spans as answers, which results in narrow answer space. |
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (N19-1)
Copied to clipboard
| Challenge: | Existing work on question-answering has limited training examples for RRC . question-announced questions are a key component of online commerce . |
| Approach: | They propose to turn customer reviews into a large source of knowledge that can be exploited to answer user questions. |
| Outcome: | The proposed approach improves review reading comprehension on popular language model BERT . it also improves aspect extraction and aspect sentiment classification tasks . |
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)
Copied to clipboard
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. |
Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)
Copied to clipboard
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. |
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have focused on continual learning of aspect sentiment classification (ASC) tasks in domain incremental learning (DIL) |
| Approach: | They propose a continual learning method that learns a sequence of tasks incrementally . they propose CLASSIC, which uses a domain incremental learning setting . |
| Outcome: | The proposed model is highly effective in a domain incremental learning setting. |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
Copied to clipboard
Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
| Approach: | They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
| Outcome: | The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to optimize target-directed molecular generation fail to reconcile conflicting objectives without compromising structural validity. |
| Approach: | They propose a condition-aware discrete diffusion framework that allows for conditional denoising guided by heterogeneous structural and property signals. |
| Outcome: | The proposed framework improves on structure-conditioned, property-conditioned and dual-conditioned benchmarks in binding affinity, drug-likeness, and success rate. |
E2EDev: Benchmarking Large Language Models in End-to-End Software Development Task (2026.acl-long)
Copied to clipboard
| Challenge: | Existing E2ESD benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols. |
| Approach: | They propose a benchmark to assess whether generated software meets user needs . they use a fine-grained set of user requirements and a fully automated testing pipeline . |
| Outcome: | E2EDev is a benchmark to assess whether generated software meets user needs through mimicking real user interactions. |
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)
Copied to clipboard
| 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. |
Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)
Copied to clipboard
Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios . |
| Approach: | They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets . |
| Outcome: | The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files. |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability. |
| Approach: | They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference. |
| Outcome: | The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability. |
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals (2022.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Progressive Planning and Reinforced Reasoning: Large Language Model-Guided Multi-hop Question Answering over Knowledge Graph (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to multi-hop question answering lack effective intermediate guidance and policy networks focus on local neighborhood information, making it difficult to anticipate the long-term consequences of decisions. |
| Approach: | They propose a framework that converts decomposed sub-question sequences into stepwise decision guidance and a structure-aware lookahead policy network to enhance the agent's global state awareness and decision foresight in complex environments. |
| Outcome: | The proposed framework surpasses state-of-the-art methods while showing strong generalization. |
Design Choices for Extending the Context Length of Visual Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing open-source Visual Language Models lack systematic exploration into extending their context length, and commercial models often provide limited details. |
| Approach: | They propose to extend Visual Language Models (VLMs) to 128K lengths and open-source the code, data, and models. |
| Outcome: | The proposed model is based on the Qwen-VL series model and is competitive with commercial model GPT-4V. |
CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing unified structured data question answering methods rely on a set of predefined functions, which restricts their ability to perform complex reasoning beyond these predefined operations. |
| Approach: | They propose a novel adaptive code-driven framework that generates code-based reasoning operations based on a question. |
| Outcome: | The proposed framework improves on multiple structured datasets on real-world scenarios. |
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing prompting techniques for Large Multi-Modal Models (LMMs) focus on improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination. |
| Approach: | They propose a prompting scheme that scaffolds coordinates to promote vision-language coordination in Large Multi-Modal Models (LMMs) they overlay a dot matrix within the image as visual information anchors and leverage multi-dimensional coordinates as textual positional references. |
| Outcome: | Experiments on a wide range of vision-language tasks show the superiority of SCAFFOLD prompting over the textual Chain-of-Thought prompting. |
Logic-Thinker: Teaching Large Language Models to Think more Logically. (2025.findings-emnlp)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users. |
| Approach: | They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives. |
| Outcome: | The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
Copied to clipboard
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 . |
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)
Copied to clipboard
Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs. |
| Approach: | They propose an efficient generative reward modeling framework grounded in model-internal uncertainty. |
| Outcome: | The proposed framework reduces inference cost while improving answer accuracy. |
Towards an On-device Agent for Text Rewriting (2024.findings-naacl)
Copied to clipboard
Yun Zhu, Yinxiao Liu, Felix Stahlberg, Shankar Kumar, Yu-Hui Chen, Liangchen Luo, Lei Shu, Renjie Liu, Jindong Chen, Lei Meng
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting, however creating a smaller yet potent language model presents two formidable challenges: costly data collection and absence of emergent capabilities. |
| Approach: | They propose a new instruction tuning method to develop a mo-bile text rewriting model that leverages LLM-generated data and heuristic reinforcement learning, eliminating the need for human data collection. |
| Outcome: | The proposed model surpasses the current state-of-the-art LLMs in text rewriting while maintaining a significantly reduced model size using public benchmark EditEval and our new benchmark. |
CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources. |
| Approach: | They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients. |
| Outcome: | The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines. |
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Adaptive Retrieval-Augmented Generation (RAG) is an effective strategy to alleviate hallucination of large language models (LLMs). |
| Approach: | They propose a novel adaptive RAG model that extracts self-aware uncertainty of large language models from their internal states and invokes retrieval accordingly. |
| Outcome: | The proposed model outperforms existing adaptive RAG methods on complex and simple Question Answering datasets. |
BloomEval: A Bloom’s Cognitive Taxonomy-Based Benchmark for Evaluating LRMs via Cognitive Hierarchy Trace (2026.findings-acl)
Copied to clipboard
| 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. |
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities. |
| Approach: | They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions. |
| Outcome: | The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset. |
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
Copied to clipboard
Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
| Challenge: | Existing information extraction (IE) tasks rely on in-context learning with large language models. |
| Approach: | They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates. |
| Outcome: | The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
Copied to clipboard
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 . |
GCPG: A General Framework for Controllable Paraphrase Generation (2022.findings-acl)
Copied to clipboard
Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen
| Challenge: | Existing studies highlight a special condition under two indispensable aspects of controllable paraphrase generation (CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness. |
| Approach: | They propose a general controllable paraphrase generation framework that integrates lexical and syntactical conditions into a text sequence and uniformly processes them in an encoder-decoder paradigm. |
| Outcome: | The proposed framework can combine lexical and syntactical conditions and improve paraphrase generation. |
DiffCL: Difference-Aware Contrastive Learning for Automatic Answer Grading with Multi-Level Semantic Modeling (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to automate answer grading lack semantic understanding and scoring consistency. |
| Approach: | They propose a difference-aware AAG framework that integrates heuristic difference labeling with dual-contrastive learning. |
| Outcome: | The proposed method outperforms cross-entropy-based baselines on SciEntsBank and Beetle datasets. |
Retrieval-Enhanced Adversarial Training for Neural Response Generation (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to dialogue systems are labor-intensive and difficult to scale up. |
| Approach: | They propose a Retrieval-Enhanced Adversarial Training method for neural response generation that leverages an adversarial training paradigm while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. |
| Outcome: | The proposed method outperforms the vanilla Seq2Seq model and conventional adversarial training approach on a large scale dataset. |
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)
Copied to clipboard
Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua
| Challenge: | Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown. |
| Approach: | They propose a benchmark for evaluating large language models using a well-organized taxonomy. |
| Outcome: | The proposed model is based on a well-organized taxonomy and compares it with other models. |
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)
Copied to clipboard
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)
Copied to clipboard
Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang
| Challenge: | Massive open online courses (MOOCs) are a popular educational platform for advanced research. |
| Approach: | They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. |
| Outcome: | The proposed datasets show that they can facilitate research in MOOCs. |
Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance. |
| Approach: | They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances. |
| Outcome: | The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset. |
LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech (2026.acl-long)
Copied to clipboard
| 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. |
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)
Copied to clipboard
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. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult. |
| Approach: | They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses . |
| Outcome: | The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata. |
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (P18-2)
Copied to clipboard
| Challenge: | Recent supervised deep learning models have achieved state-of-the-art performance, but there are two other considerations that are important. |
| Approach: | They propose a supervised aspect extraction model using general-purpose embeddings and domain-specific embeddables. |
| Outcome: | The proposed model outperforms state-of-the-art methods without supervision and achieves very good results. |
ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)
Copied to clipboard
| 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. |
Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)
Copied to clipboard
Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin
| Challenge: | Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding. |
| Approach: | They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications. |
| Outcome: | The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. |
TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
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)
Copied to clipboard
| Challenge: | Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection. |
| Approach: | They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM. |
| Outcome: | The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead. |
Augmenting Legal Judgment Prediction with Contrastive Case Relations (2022.coling-1)
Copied to clipboard
| Challenge: | Existing legal judgment prediction methods only consider one case fact description as input, which may not fully utilize information in the data such as case relations and frequency. |
| Approach: | They propose a new perspective that introduces some contrastive case relations to construct case triples as input and a corresponding judgment prediction framework with case triple modeling. |
| Outcome: | The proposed framework can be used to refine encoding and decoding processes using three customized modules on two public datasets. |
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision (2025.emnlp-industry)
Copied to clipboard
Yongqi Leng, Yikun Lei, Xikai Liu, Meizhi Zhong, Bojian Xiong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Recent advances in outcome-supervised reinforcement learning (RL) have shown strong performance, but this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback. |
| Approach: | They propose a model that models RAG as a Markov Decision Process (MDP) and introduces an efficient pruning strategy to optimize data expansion. |
| Outcome: | The proposed model outperforms existing methods and achieves an average performance improvement of 6.2% across six datasets. |
UnrealLLM: Towards Highly Controllable and Interactable 3D Scene Generation by LLM-powered Procedural Content Generation (2025.findings-acl)
Copied to clipboard
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. |
Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming (2026.findings-acl)
Copied to clipboard
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. |
Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)
Copied to clipboard
| Challenge: | Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks. |
| Approach: | They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese. |
| Outcome: | The proposed model improves the few-shot table understanding in Chinese. |
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese. |
| Approach: | They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models. |
| Outcome: | The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show. |
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing arguments generation methods often overlook connections between opinions . Existing methods struggle with providing compelling proof . |
| Approach: | They propose a two-stage framework for argumentative essay generation with a focus on logical enhancement. |
| Outcome: | The proposed framework generates argumentative essays with better logical validity and persuasiveness than baseline models. |
Diagnosing Moral Reasoning Acquisition in Language Models: Pragmatics and Generalization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Prior research has shown that LLMs fail to perform satisfactorily on moral cognizance tasks . |
| Approach: | They propose to use curated datasets to improve LLMs' moral cognizance . they find pragmatic dilemma constrains generalization ability of current learning paradigms . |
| Outcome: | The proposed learning paradigms fail to perform on moral cognizance tasks, the authors show . they show that the pragmatic dilemma is the primary bottleneck for moral reasoning acquisition . |
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)
Copied to clipboard
Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin
| Challenge: | Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations. |
| Approach: | They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness. |
| Outcome: | The proposed approach outperforms existing methods while achieving superior editing efficiency. |
Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)
Copied to clipboard
Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss. |
| Approach: | They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant. |
| Outcome: | The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting. |
Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations (2024.acl-long)
Copied to clipboard
| Challenge: | Discursive Socratic Questioning (DISQ) assesses a model's understanding of discourse relations by requiring systematic accuracy over multiple questions. |
| Approach: | They propose a method that evaluates faithfulness of understanding discourse based on question answering. |
| Outcome: | The proposed method evaluates the faithfulness of understanding discourse based on question answering. |
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios (2024.findings-acl)
Copied to clipboard
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)
Copied to clipboard
Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for document-level event extraction are not effective due to two challenges . existing methods fail to extract events whose arguments spread in multiple sentences . |
| Approach: | They propose a document-level event extraction model with a tracker to capture interdependency among the extracted events. |
| Outcome: | The proposed model outperforms existing models on a large-scale dataset by 2.8 F1 . it extracts multiple correlated events and event arguments that scatter across the document . |
One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)
Copied to clipboard
Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li
| Challenge: | Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality. |
| Approach: | They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. |
| Outcome: | Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks. |
Human or LLM as Standardized Patients? A Comparative Study in Medical Education (2026.acl-long)
Copied to clipboard
| 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. |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
Copied to clipboard
| 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. |
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for enhancing the performance of large language models require expensive manual annotations. |
| Approach: | They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence. |
| Outcome: | The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method. |
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing (2022.emnlp-demos)
Copied to clipboard
Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
| Challenge: | Pre-Trained Models (PTMs) have reshaped the development of natural language processing (NLP) but it is not easy to obtain high-performing PTMs without a large amount of labeled training data and deploy them online with fast inference speed. |
| Approach: | They propose to make it easy to build NLP applications with knowledge-enhanced pre-training and knowledge distillation. |
| Outcome: | EasyNLP supports a comprehensive suite of NLP algorithms and features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities. |
Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering. |
| Approach: | They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths . |
| Outcome: | The proposed method surpasses existing methods on knowledge-intensive multi-hop questions. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
Copied to clipboard
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)
Copied to clipboard
Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context. |
| Approach: | They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts. |
| Outcome: | The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context. |
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective (2025.coling-main)
Copied to clipboard
| Challenge: | Enabling LLMs to handle lengthy context is currently a research hotspot . a notable challenge limiting further customization is the inability of LLM to utilize context beyond pretrained length due to the inherent flaw of rotary position embedding (RoPE). |
| Approach: | They propose to extend the RoPE from an attention perspective and on two benchmarking tasks. |
| Outcome: | The proposed extension of the RoPE improves extrapolation and retrieval errors. |
PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents (2026.acl-long)
Copied to clipboard
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. |
ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to measuring and optimizing proactive task-oriented agents lack generalizable end-to-end solutions. |
| Approach: | They propose a framework for conversational task scheduling that integrates proactiveness reinforcement learning with a domain-agnostic annotation methodology. |
| Outcome: | The proposed framework enables scalable proactiveness reinforcement learning (RL) Experiments on two newly auto-annotated datasets demonstrate significant improvements in proactive timing while maintaining action consistency comparable to state-of-the-art baselines. |
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing. |
| Approach: | They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs. |
| Outcome: | The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge. |
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)
Copied to clipboard
Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks. |
| Approach: | They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions. |
| Outcome: | The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. |
Fusion-Eval: Integrating Assistant Evaluators with LLMs (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks. |
| Approach: | They propose a method that leverages large language models to integrate insights from various assistant evaluators. |
| Outcome: | The proposed approach achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.7444 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods. |
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)
Copied to clipboard
| Challenge: | Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts. |
| Approach: | They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls. |
| Outcome: | The results show that the evaluations are reliable and lack pipeline evaluations. |
S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
Copied to clipboard
Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
| Challenge: | Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks. |
| Approach: | They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents . |
| Outcome: | The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%. |
Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding (2024.acl-long)
Copied to clipboard
| Challenge: | Generating long-term texts using artificial intelligence has always been a challenge . however, the generated novels exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. |
| Approach: | They propose a method for extracting excelsior and expanding from novel data to generate arbitrarily long novels using large language models. |
| Outcome: | The proposed method produces high-quality long-form novels with a high level of logical coherence and appeal despite the use of large language models. |
PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current 3D medical imaging models focus on spatial features, neglecting phase-specific progression detailed in clinical reports. |
| Approach: | They propose a framework that fuses imaging phases with clinical text to enhance 3D medical image retrieval. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a phase-series dataset of 12,230 hospital CT scans. |
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
Copied to clipboard
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. |
Neural Collective Entity Linking (C18-1)
Copied to clipboard
| Challenge: | Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently. |
| Approach: | They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency. |
| Outcome: | The proposed model improves its performance on five publicly available datasets and can be used to train on Wikipedia hyperlinks to avoid overfitting and domain bias. |
Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models have demonstrated superior performance on various natural language processing tasks. |
| Approach: | They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training . |
| Outcome: | The neurons are highly predictive of task labels after prompt tuning for specific tasks. |
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have identified critical layers linked to specific functions or behaviors, limiting their use to post-hoc settings. |
| Approach: | They propose a data-oblivious approach to identify intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered Kernel Alignment. |
| Outcome: | The proposed approach identifies critical layers in pre-fine-tuned models . layers with significant shifts in representation space are also those most affected during fine-tuning . |
ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom (2025.emnlp-main)
Copied to clipboard
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. |
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)
Copied to clipboard
Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao
| Challenge: | Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms. |
| Approach: | They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content. |
| Outcome: | The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs. |
Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time. |
| Approach: | They propose a method that uses a pre-trained continuous vector to generate a fixed pre-trainable language model to satisfy a specified attribute. |
| Outcome: | The proposed model can achieve improvements on eleven attribute-specific generation tasks with 0.08% extra training parameters. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
Copied to clipboard
Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)
Copied to clipboard
Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li
| Challenge: | Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored. |
| Approach: | They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation. |
| Outcome: | The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs. |
DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Chinese document-level event extraction is still largely unexplored. |
| Approach: | They propose a Chinese document-level event extraction dataset with over 36,000 events and 210,000 arguments. |
| Outcome: | The proposed dataset includes over 36,000 events and more than 210,000 arguments . it is an extension of the DocEE dataset, utilizing the same event schema and annotated by human experts. |
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable . |
| Approach: | They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark . |
| Outcome: | The proposed framework outperforms existing models in terms of performance and interpretability. |
Explainable Text Classification with LLMs: Enhancing Performance through Dialectical Prompting and Explanation-Guided Training (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing explanation methods that generate keywords may be less effective due to missing critical contextual information. |
| Approach: | They propose a new method to generate explanations for possible labels using LLMs and a dialectical prompt. |
| Outcome: | The proposed method significantly improves accuracy and explanation quality over state-of-the-art methods on multiple datasets from diverse domains. |
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure (2020.coling-main)
Copied to clipboard
| 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. |
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB. |
| Approach: | They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities . |
| Outcome: | The proposed framework can achieve superior performance over the state of the arts. |
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing pre-trained MLMs produce an anisotropic distribution of token representations . this is not ideal for tasks that require discriminative semantic meanings of distinct tokens - a problem that exists in pre-training models . |
| Approach: | They propose a continual pre-training approach that encourages BERT to learn an isotropic distribution of token representations. |
| Outcome: | The proposed approach improves on a wide range of English and Chinese benchmarks. |
Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for training large language models rely heavily on high-quality parallel data, which are often scarce or unavailable for low-resource languages. |
| Approach: | They propose a reinforcement training method using only monolingual text to elevate LLMs’ translation capabilities on massive low-resource languages while retaining their performance on high-resourced languages. |
| Outcome: | The proposed model outperforms LLaMAX, one of the strongest open-source multilingual LLMs on 1,414 language directions on Flores-101 dataset. |
Pretraining the Noisy Channel Model for Task-Oriented Dialogue (2021.tacl-1)
Copied to clipboard
| Challenge: | Current research on task-oriented dialogue models suffers from the explaining-away effect, manifested in models that favor short and generic responses. |
| Approach: | They propose to factorize the dialogue task into two models, the distribution of the context given the response, and the prior for the response itself, using Bayes' theorem. |
| Outcome: | The proposed model mitigates the explaining-away effect and allows the principled incorporation of large pretrained models for the response prior. |
Marathon: A Race Through the Realm of Long Context with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing long-context benchmarks do not accurately evaluate large language models’ comprehension and reasoning abilities in extended texts. |
| Approach: | They propose a new evaluation benchmark that adopts a multiple-choice question format and uses a multi-choke question format to assess the comprehension and reasoning skills of large language models. |
| Outcome: | The proposed benchmark provides a rapid, precise, and unbiased appraisal of the long-context comprehension skills of large language models. |
Length Controlled Generation for Black-box LLMs (2025.acl-long)
Copied to clipboard
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. |
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance . |
| Approach: | They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences . |
| Outcome: | The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs. |
Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding (2023.findings-acl)
Copied to clipboard
Mengyue Zhou, Xu Liu, David Liu, Zihao Wu, Zhengliang Liu, Lin Zhao, Dajiang Zhu, Lei Guo, Junwei Han, Tianming Liu, Xintao Hu
| Challenge: | Existing studies treat each transformer encoding layer as a single artificial neuron . layer-level embeddings aggregate multiple types of contextual attention captured by multiple head modules . |
| Approach: | They propose to embed each transformer encoding layer as a single artificial neuron . they propose to couple those ANs with their biological-neuron counterparts in the human brain . |
| Outcome: | The proposed models can be used to link representations to brain activity, the authors say . their results show that the proposed models carry meaningful neurolinguistic information . |
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)
Copied to clipboard
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. |
A Split-and-Recombine Approach for Follow-up Query Analysis (D19-1)
Copied to clipboard
| Challenge: | Context-dependent semantic parsing has proved to be an important but challenging task. |
| Approach: | They propose to perform follow-up query analysis to restate context-dependent queries with contextual information. |
| Outcome: | The proposed approach outperforms the state-of-the-art by nearly 8% on the FollowUp dataset . the extensibility of STAR on the SQA dataset is also promising . |
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)
Copied to clipboard
| 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)
Copied to clipboard
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)
Copied to clipboard
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. |
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)
Copied to clipboard
Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu
| Challenge: | Existing toolsets that use large language models are limited to single-task settings. |
| Approach: | They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. |
| Outcome: | The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. |
LayerNorm Induces Recency Bias in Transformer Decoders (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that stacking causal self-attention layers alone induces a positional bias in attention scores toward earlier tokens, but this differs from the bias toward later tokens observed in Transformer decoders, known as recency bias. |
| Approach: | They propose to stack causal self-attention layers and layer norm to induce recency bias in Transformer decoders by analyzing the interaction between causal self and other architectural components. |
| Outcome: | The proposed method provides new theoretical insights into how positional information interacts with architectural components and suggests improvements in positional encoding strategies. |
An Efficient Conversational Smart Compose System (2023.acl-demo)
Copied to clipboard
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. |
Red Teaming Visual Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | VLMs (Vision-Language Models) can be induced to generate harmful or inaccurate content through specific test cases. |
| Approach: | They propose a red teaming dataset which encompasses 12 subtasks under 4 primary aspects (faithfulness, privacy, safety, fairness) this dataset is the first to benchmark current VLMs in terms of these 4 aspects . |
| Outcome: | The proposed dataset shows that 10 open-source VLMs struggle with red teaming in different degrees and have up to 31% performance gap with GPT-4V. |
METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues (2026.acl-long)
Copied to clipboard
| Challenge: | Developing non-collaborative dialogue agents traditionally requires manual codification of expert strategies. |
| Approach: | They propose a method that formalizes expert knowledge into a Strategy Forest from raw transcripts. |
| Outcome: | The proposed method outperforms existing methods by 9%-10% in two benchmarks. |
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models (2022.emnlp-main)
Copied to clipboard
| 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. |
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for extracting aspects and opinions from text are incomplete. |
| Approach: | They propose a method for extracting Implicit Aspects with Categories and Opinions with Sentiments using implicit tokens. |
| Outcome: | The proposed method outperforms baseline methods on two public benchmark datasets. |
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
Copied to clipboard
Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
| Approach: | They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story. |
| Outcome: | The proposed approach outperforms existing methods in 84.33% of the trials. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
Copied to clipboard
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. |
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)
Copied to clipboard
| Challenge: | Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. |
| Approach: | They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. |
| Outcome: | The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%. |
Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)
Copied to clipboard
Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo
| Challenge: | Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. |
| Approach: | They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance. |
| Outcome: | The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50. |
QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)
Copied to clipboard
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)
Copied to clipboard
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. |
TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of Behavioral Therapy Notes (2025.acl-industry)
Copied to clipboard
| Challenge: | Behavioral therapy notes are important for legal compliance and patient care, but quality standards for them remain underdeveloped. |
| Approach: | They propose a rubric for evaluating therapy notes across key dimensions: completeness, conciseness, faithfulness. |
| Outcome: | The proposed evaluation framework improves on therapist-written notes and LLM-generated notes. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
Copied to clipboard
Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
Dynamic Voting for Efficient Reasoning in Large Language Models (2023.findings-emnlp)
Copied to clipboard
Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Xingzhang Ren, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Multi-path voting methods generate multiple reasoning paths for each problem, causing factual errors and illusion generation. |
| Approach: | They propose a multi-path voting technique that effectively reduces the number of reasoning paths during multi-path voting while preserving accuracies. |
| Outcome: | The proposed method outperforms Self-consistency using 24.7% of the number of paths on the LetterConcat task. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
Copied to clipboard
Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
A Systematic Investigation of KB-Text Embedding Alignment at Scale (2021.acl-long)
Copied to clipboard
| Challenge: | Knowledge bases (KBs) and text often contain complementary knowledge. |
| Approach: | They propose a framework for aligning KB and text embeddings for joint reasoning . they also evaluate alignment methods to infuse textual information into KB embeddables . |
| Outcome: | The proposed framework can be used to predict link prediction on emerging entities and events using textual information. |
S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)
Copied to clipboard
| Challenge: | Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities . |
| Approach: | They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner. |
| Outcome: | The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard. |
ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies on human-LLM competitive programming use scattered, application-specific human feedback. |
| Approach: | They propose a taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation. |
| Outcome: | The proposed benchmark pinpoints strengths and weaknesses of existing methods and will be openly released. |
Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)
Copied to clipboard
Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li
| Challenge: | Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. |
| Approach: | They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching. |
| Outcome: | The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions. |
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to classify aspects with aspect sentiment bias are hard to find . |
| Approach: | They propose a no-aspect differential sentiment framework for the ABSA task that eliminates aspect sentiment bias and uses differential sentiment loss instead of cross-entropy loss to better classify the sentiments. |
| Outcome: | The proposed framework can be combined with almost all traditional ABSA methods. |
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)
Copied to clipboard
Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases. |
| Approach: | They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities. |
| Outcome: | The proposed model outperforms open-source models but struggles on longer contexts. |
Speeding Up Neural Machine Translation Decoding by Cube Pruning (D18-1)
Copied to clipboard
| Challenge: | Neural machine translation suffers from slow translation speed due to the large search space . a trade-off has to be made between translation quality and speed, argues a new study . |
| Approach: | They apply cube pruning technique to speed up dynamic programming into neural machine translation to speed it up. |
| Outcome: | The proposed method can translate faster on GPUs and CPUs with better translation quality than naive beam search. |
UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)
Copied to clipboard
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. |
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)
Copied to clipboard
Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao
| Challenge: | Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness . |
| Approach: | They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization. |
| Outcome: | The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets. |
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for machine reading comprehension of user manuals have trouble answering complex questions. |
| Approach: | They propose a knowing-how & knowing-that task that requires the model to answer factoid-style, procedure-style and inconsistent questions about user manuals. |
| Outcome: | The proposed model can answer factoid-style, procedure-style and inconsistent questions about user manuals. |
Towards Effective Automatic Debt Collection with Persona Awareness (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Existing debt collection agents fail to tailor strategies to debtor personas, leading to ineffective collection. |
| Approach: | They present a commercial practice on debt collection agents that organizes debtor personas into a taxonomy and constructs a persona-aware conversation dataset. |
| Outcome: | The proposed agent increases recovery rate by 3.31% and collects additional 100K RMB after two months of testing. |
WantWords: An Open-source Online Reverse Dictionary System (2020.emnlp-demos)
Copied to clipboard
| Challenge: | Existing reverse dictionary systems only support English reverse dictionary queries . a reverse dictionary can help people who can't remember a word from memory . |
| Approach: | They propose an online reverse dictionary system that outperforms other reverse dictionary systems . it supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries . |
| Outcome: | The proposed reverse dictionary outperforms other reverse dictionary systems on performance . it supports Chinese and English-Chinese as well as Chinese-English queries . |
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition (2021.acl-long)
Copied to clipboard
| 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. |
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)
Copied to clipboard
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)
Copied to clipboard
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)
Copied to clipboard
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. |
ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
| Approach: | They propose a method that leverages layer uncertainty to allocate budget size for each layer to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage of the KV caches to only 20% when compared to full KV inference while achieving nearly lossless performance. |
Croppable Knowledge Graph Embedding (2025.acl-long)
Copied to clipboard
| Challenge: | Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks. |
| Approach: | They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs. |
| Outcome: | The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity. |
From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)
Copied to clipboard
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. |
Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)
Copied to clipboard
| Challenge: | Current error-handling works are performed in a passive manner, with explicit error- handling instructions. |
| Approach: | They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research. |
| Outcome: | The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances. |
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models struggle to meet user’s needs when required to generate responses of a specific length due to their inherent difficulty in accurately perceiving numerical constraints. |
| Approach: | They propose a Target Length Generation Task and propose RULER, a model-agnostic approach that controls generated length for large language models. |
| Outcome: | The proposed model-agnostic approach improves instruction-following ability of large language models under length-constrained instructions and can generate appropriate MLT when length constraints are not explicitly provided. |
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback (2024.findings-naacl)
Copied to clipboard
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. |
HAUNTATTACK: When Attack Follows Reasoning as a Shadow (2026.findings-acl)
Copied to clipboard
| Challenge: | Emerging Large Reasoning Models (LRMs) excel in mathematical and reasoning tasks, showcasing remarkable capabilities. |
| Approach: | They propose a framework that embeds harmful instructions into reasoning questions . they evaluate 11 LRMs and observe an average attack success rate of over 70% . |
| Outcome: | The proposed framework improves reasoning models by 13 percentage points over baseline. |
Modeling Multi-Action Policy for Task-Oriented Dialogues (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to learn dialogue management only predict one action per turn, limiting expressive power of the conversational agent and introducing unnecessary turns of interactions. |
| Approach: | They propose a model based on a recurrent cell called gated Continue-Act-Slots that overcomes the limitations of existing models and proposes a novel policy model that predicts multiple acts for each turn. |
| Outcome: | The proposed model outperforms existing models on the task of predicting multiple acts for each turn. |
Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)
Copied to clipboard
| 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 . |
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)
Copied to clipboard
Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu
| Challenge: | DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks. |
| Approach: | They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks. |
| Outcome: | The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks . |
CARE: A Clue-guided Assistant for CSRs to Read User Manuals (2024.acl-long)
Copied to clipboard
| Challenge: | Current solutions don't fit the online custom service scenarios well due to the lack of attention to user questions and possible responses. |
| Approach: | They propose to build a clue-guided assistant for customer service representations (CSRs) that can provide accurate responses and explicitly show explainable paths about how to arrive at these responses. |
| Outcome: | The proposed assistant can reduce CSRs' reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score. |
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies show impressive results on aspects-based sentiment analysis tasks. |
| Approach: | They analyze the attentions and learned representations of BERT for aspects-based sentiment analysis tasks. |
| Outcome: | The proposed model can be used for aspects-based sentiment analysis (ABSA) but it is not clear how it can provide important features for downstream tasks. |
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)
Copied to clipboard
| Challenge: | Existing models for text-to-SQL do not explicitly introduce common knowledge to address comparison relations. |
| Approach: | They propose to leverage adjective-noun phrasing knowledge mined from the web to predict comparison relations in text-to-SQL. |
| Outcome: | The proposed approach improves on the original and re-split Spider datasets on comparison relation prediction. |
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | RAG implementations face challenges in addressing retrieved noise and redundant content . current RAG methods lack the ability to exploit fine-grained inter-document relationships . |
| Approach: | They propose a retrieval-augmented generation framework that exploits latent inter-document relationships while removing irrelevant information and redundant content. |
| Outcome: | The proposed framework achieves consistent performance improvements on knowledge-QA and hallucination-Detection datasets. |
Learning from Textual Radiology Reports: A Benchmark Dataset for Coronary CT Angiography (2026.acl-industry)
Copied to clipboard
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. |
POS-Constrained Parallel Decoding for Non-autoregressive Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing non-autoregressive generation systems face multimodality problem due to conditionally independent decoding. |
| Approach: | They propose to incorporate linguistic structure into NAG inference instead of teacher AG . they propose a method that provides a specific POS sequence to constrain the NAG model . |
| Outcome: | The proposed method improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. |
Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)
Copied to clipboard
| 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. |
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
Copied to clipboard
| Challenge: | Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference. |
| Approach: | They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning. |
| Outcome: | The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios. |
From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)
Copied to clipboard
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. |
Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization (2026.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that KV cache compression can increase hallucination scores in LLMs . modern LLM models support extremely long sequences, but their impact on model hallucinosity remains underexplored. |
| Approach: | They propose a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context. |
| Outcome: | The proposed method reduces hallucination across multiple models and datasets while preserving computational efficiency. |
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)
Copied to clipboard
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)
Copied to clipboard
| Challenge: | Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance. |
| Approach: | They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results. |
| Outcome: | The proposed method improves on the datasets from Coursera and XuetangX. |
Adapting a Language Model While Preserving its General Knowledge (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing DA-training methods do not explicitly identify what knowledge should be preserved and what should be changed by the domain corpus. |
| Approach: | They propose to use an unlabeled corpus of aparticular domain to train a pre-trained general-purpose language model to adapt the LM so that end-tasks in the domain can give improved performances. |
| Outcome: | The proposed method improves the performance of pre-trained general-purpose language models by contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and specific knowledge. |
ImgTrojan: Jailbreaking Vision-Language Models with ONE Image (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on the safety of large language models (LLMs) with human values have focused on the integration of multi-modal user input into these models. |
| Approach: | They propose a method to bypass safety constraints of large language models by using poisoned images instead of original textual captions. |
| Outcome: | The proposed attack bypasses safety constraints of large language models (VLMs) by replacing the original textual captions with malicious jailbreak prompts. |
Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)
Copied to clipboard
Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive. |
| Approach: | They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources. |
Can an Individual Manipulate the Collective Decisions of Multi-Agents? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that coordinated multi-agent systems exhibit enhanced decision-making and reasoning abilities through collaboration. |
| Approach: | They propose a framework that simulates agent interactions within a multi-agent system to generate adversarial samples and use them to manipulate the target agent in the target system. |
| Outcome: | The proposed framework generates adversarial samples that are used to manipulate the target agent in the target system, misleading the system’s decision-making process. |
Enhancing Reinforcement Learning with Dense Rewards from Language Model Critic (2024.emnlp-main)
Copied to clipboard
| Challenge: | Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences, but the sparsity of these signals can lead to inefficient and unstable learning. |
| Approach: | They propose a framework that utilizes the critique capability of Large Language Models to produce intermediate-step rewards during RL training. |
| Outcome: | The proposed framework improves sample efficiency and the overall performance of the policy model, supported by both automatic and human evaluation. |
Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)
Copied to clipboard
Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen
| Challenge: | Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem. |
| Approach: | They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces. |
| Outcome: | The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems. |
Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing definition generation methods take the source word as an indecomposable semantic unit, but in parataxis languages like Chinese, word meanings can be composed using the word formation process. |
| Approach: | They propose to use word formation features to enhance Definition Generation (DG) in Chinese to generate an explanatory text. |
| Outcome: | The proposed model enhances Definition Generation (DG) in Chinese by decomposing the word meaning into different semantic components. |
Can Language Models Understand Physical Concepts? (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing language models do not understand basic physical concepts in the human world. |
| Approach: | They propose a method to transfer embodied knowledge from visual models to LMs . they use visual concepts and embodies concepts learned from interaction with the world . |
| Outcome: | The proposed method achieves comparable performance with scaling up parameters of LMs 134. |
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting (2023.findings-acl)
Copied to clipboard
| 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. |
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)
Copied to clipboard
Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang
| Challenge: | Current document image parsing solutions rely on specialized models or generate content autoregressively. |
| Approach: | They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation. |
| Outcome: | The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency. |
Speak No Evil, Just Prompt: Low-resource Multilingual Toxic Speech Detection with Audio Language Model (2026.findings-acl)
Copied to clipboard
| 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 . |
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)
Copied to clipboard
Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen
| Challenge: | Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance. |
| Approach: | They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents. |
| Outcome: | The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain. |
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the results are reliable. |
| Approach: | They propose a framework for human evaluation of generative large language models that takes into account usability, aesthetics and cognitive biases. |
| Outcome: | The proposed framework is based on the framework proposed by Deutsch and alnajjar . it is aimed at ensuring that human evaluation is accurate in the age of generative AI . |
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies have explained to what extent LLMs extract conflicting knowledge from the provided text, but they neglect the necessity to reason with conflicting information. |
| Approach: | They construct a dataset for knowledge conflict resolution examination in the form of question answering that divides reasoning with conflicting knowledge into three levels. |
| Outcome: | The proposed dataset enables analysis of reasoning with conflicting knowledge in the form of question answering. |
LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
| Approach: | They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system. |
| Outcome: | The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance. |
KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)
Copied to clipboard
Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu
| Challenge: | Recent advances in pre-trained language models have made it possible to generate human-like text. |
| Approach: | They propose to integrate an open-ended text adventure game in Chinese, named KuiLeiXi, where players interact with the AI until the plot goals are reached. |
| Outcome: | The proposed game lacks incentives and relies on players to explore on their own. |
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that sequence-to-sequence (seq2sequ) models struggle with compositional generalization (CG) a crucial property of human language learning is its compositional globalization (GC), the algebraic ability to understand and produce a potentially infinite number of novel combinations from known components. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) extension which learns to compose representations of different encoder layers dynamically for different tasks. |
| Outcome: | The proposed model achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of the proposed model. |
Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens (2024.findings-emnlp)
Copied to clipboard
| 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. |
Proofread: Fixes All Errors with One Tap (2024.acl-demos)
Copied to clipboard
Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai
| Challenge: | Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio. |
| Approach: | They propose a two-stage tuning approach to acquire the dedicated Large Language Model for the feature, followed by a reinforcement learning approach for targeted refinement. |
| Outcome: | The proposed model achieves 85.56% good quality on Rewrite and proofread tasks on human-labeled golden sets. |
PaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers. |
| Approach: | They propose a multi-modal multi-document benchmark for agentic deep research that integrates evidence from multiple documents. |
| Outcome: | Experimental results show that even advanced systems achieve limited scores on PaperScope . paper provides a rigorous benchmark alongside a pipeline for constructing large multi-modal, multi-source deep research datasets. |
LEGENT: Open Platform for Embodied Agents (2024.acl-demos)
Copied to clipboard
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. |