Papers by Li Peng
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. |
| Approach: | They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. |
| Outcome: | The proposed model improves performance under both offline and online learning strategies. |
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)
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| Challenge: | Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. |
| Approach: | They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters. |
| Outcome: | The proposed model demonstrates comparable performance on multiple benchmarks. |
Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping (2026.findings-acl)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs’ short-context reasoning but falters in long-contemporal scenarios requiring precise grounding and multi-hop reasoning. |
| Approach: | They propose a framework that constructs high-difficulty, multi-hop long-context QA pairs with inherent reasoning chains to overcome this bottleneck. |
| Outcome: | The proposed framework outperforms RLVR baselines and matches frontier LLMs while using far fewer parameters. |
Towards LLM-powered Attentive Listener: A Pragmatic Approach through Quantity Self-Repair (2025.acl-short)
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| Challenge: | Quantity Maxims dictates that human speakers aim for optimal quantity of information during conversation. |
| Approach: | They propose to use heuristic path-finding to enable decoder-only LLMs to travel among multiple "Q-alternatives" and search for optimal quantity in coordination with a conversation goal. |
| Outcome: | The proposed techniques are based on heuristic path-finding and can be used to construct human-like, user-centered conversation agents. |
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (2022.acl-long)
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| Challenge: | Different Open Information Extraction (OIE) tasks require different types of information. |
| Approach: | They propose to adapt an OIE Graph to different OIE tasks with simple rules . they implement an end-to-end OIA generator and make it open-accessible . |
| Outcome: | The proposed system achieves new SOTA performance on three popular OIE tasks. |
On the Analysis and Distillation of Emergent Outlier Properties in Pre-trained Language Models (2025.naacl-long)
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| Challenge: | Existing studies show that a small subset of dimensions within language Transformers’ representation spaces emerge as "outliers" during pretraining. |
| Approach: | They propose a method that prioritizes critical outlier dimensions in distillation using a weighted MSE loss. |
| Outcome: | The proposed method outperforms state-of-the-art distillation methods and generalizes well across Encoder-only BERT, Decoder-only GPT-2, and Encodeer-Decoder T5 architectures. |
AT²PO: Agentic Turn-based Policy Optimization via Tree Search (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have catalyzed the development of autonomous agents capable of executing complex, multi-turn tasks. |
| Approach: | They propose a framework for agentic reinforcement learning that integrates turn-level tree search with tree search to address key challenges. |
| Outcome: | The proposed framework addresses key challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. |
Model Composition for Multimodal Large Language Models (2024.acl-long)
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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities. |
| Approach: | They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters. |
| Outcome: | The proposed model retains the modal understanding capabilities of each original model. |
M3Seg: A Maximum-Minimum Mutual Information Paradigm for Unsupervised Topic Segmentation in ASR Transcripts (2023.emnlp-main)
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| Challenge: | Topic segmentation aims to split automatic speech recognition transcriptions into segments that are bounded by thematic meanings. |
| Approach: | They propose a Maximum-Minimum Mutual information paradigm for linear topic segmentation without using any parallel data. |
| Outcome: | The proposed paradigm outperforms the state-of-the-art methods by a significant margin. |
Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations (2026.acl-long)
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Wen Luo, Guangyue Peng, Wei Li, Shaohang Wei, Feifan Song, Liang Wang, Nan Yang, Xingxing Zhang, Jing Jin, Furu Wei, Houfeng Wang
| Challenge: | Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear. |
| Approach: | They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer . |
| Outcome: | The proposed applications improve hallucination detection performance by integrating two different inputs. |
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage (2026.findings-acl)
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Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie
| Challenge: | Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. |
| Approach: | They propose an algorithm that detects threshold where information loss exceeds information gain during sparse decoding to reduce token consumption by up to 90% and a marginal accuracy degradation of less than 2%. |
| Outcome: | The proposed algorithm reduces token consumption by 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. |
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2 (2021.acl-srw)
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| Challenge: | Experimental results show that pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
| Approach: | They conduct experiments on an English essay dataset using Chinese-GPT2 . they find that the model can generate better continuations by learning to generate the in the fine-tuning stage. |
| Outcome: | The pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval (2022.findings-naacl)
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| Challenge: | Existing multilingual video corpus moment retrieval methods are based on a two-stream structure. |
| Approach: | They propose a multilingual video corpus moment retrieval task that uses a two-stream structure to generate a query-visual similarity and a subtitle stream exploits the query-subtitle similarity. |
| Outcome: | The proposed method improves accuracy on a large-scale video corpus moment retrieval dataset. |
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)
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| Challenge: | Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data. |
| Approach: | They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. |
| Outcome: | The proposed language model generalizes well across knowledge-grounded dialogue tasks. |
SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval (2026.findings-acl)
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| Challenge: | a new framework casts LLM planning as non-parametric retrieval, but high latency of inference-time search and supervised fine-tuning are limitations. |
| Approach: | They propose a framework that casts LLM planning as non-parametric retrieval . they leverage Monte Carlo Tree Search to explore the solution space . |
| Outcome: | Empirical results show that SGA-MCTS can match the performance of SOTA systems without task-specific fine-tuning. |
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models (2026.acl-long)
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Zhiwen Ruan, Yichao Du, Jianjie Zheng, Longyue Wang, Yun Chen, Peng Li, Jinsong Su, Yang Liu, Guanhua Chen
| Challenge: | Existing adapter-based transfer methods treat instruction-tuned models as passive targets . direct fine-tuning can disrupt this delicate balance and lead to instability or performance degradation. |
| Approach: | They propose a framework that incorporates instruction-level guidance into task adaptation. |
| Outcome: | The proposed framework outperforms direct fine-tuning and representative transfer-based baselines while maintaining robust generalization and favorable test-time scaling behavior. |
RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems (2026.findings-acl)
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Jiacheng Liu, Zichen Tang, Zhongjun Yang, Xinyi Hu, Xueyuan Lin, Linwei Jia, Ruofei Bai, Rongjin Li, Shiyao Peng, Haocheng Gao, Haihong E
| Challenge: | Existing tools to generate structured content for research tasks are limited in their ability to generate high-quality roadmaps. |
| Approach: | They propose a benchmark to evaluate the ability of large language models (LLMs) to generate high-quality roadmaps for solving complex research problems. |
| Outcome: | The proposed system can improve LLMs’ ability for roadmap generation while saving 84% of the time required by human experts. |
Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction (2025.findings-acl)
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| Challenge: | a novel method for encoding fine-grained error patterns improves performance on GEC. |
| Approach: | They propose a method for encoding grammatical errors from LLMs' internal states using a GER method. |
| Outcome: | The proposed method significantly boosts performance in ICL settings on multilingual GEC datasets. |
EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information (2024.findings-emnlp)
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| Challenge: | Existing methods for metaphor detection rely on heuristics such as Metaphor Identification Procedure (MIP) and Selection Preference Violation (SPV). |
| Approach: | They propose a cognitively motivated module that leverages the cognitive information of embodiment that can be derived from word embeddings and explicitly models the process of sensorimotor change that has been demonstrated as essential for metaphor processing. |
| Outcome: | The proposed module can improve metaphor detection compared with the heuristic MIP that has been applied previously. |
Contextualized Perturbation for Textual Adversarial Attack (2021.naacl-main)
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| Challenge: | Existing techniques for generating adversarial examples are driven by local heuristic rules that are agnostic to the context, resulting in unnatural and ungrammatical outputs. |
| Approach: | They propose a ContextuaLized AdversaRial Example generation model that generates fluent and grammatical outputs through a mask-then-infill procedure. |
| Outcome: | The proposed model outperforms baseline models in terms of attack success rate, textual similarity, fluency and grammaticality. |
Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training (2025.acl-long)
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| Challenge: | Existing methods to optimise pretraining performance have not addressed the complexities of domain-adaptive continual pretraining. |
| Approach: | They propose a framework that dynamically assesses learning velocity and adjusts data proportions accordingly, favouring slower learning domains while de-emphasising faster learning ones. |
| Outcome: | The proposed framework achieves performance gains in math and code reasoning tasks and command-line generation benchmarks. |
Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors (2026.findings-acl)
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Fuwen Luo, Zihao Wan, Ziyue Wang, Yaluo Liu, Pau Tong Lin Xu, Xuanjia Qiao, Xiaolong Wang, Peng Li, Yang Liu
| Challenge: | Existing structural analysis methods for hieroglyphic scripts are script-specific and labor-intensive. |
| Approach: | They propose a hieroglyphic Stroke Analyzer framework that captures character-internal structures and semantics without handcrafted data. |
| Outcome: | The proposed framework captures character-internal structures and semantics without priors . it can be used to generalize hieroglyphic scripts across languages . |
ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models (2025.acl-long)
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Ziyue Wang, Chi Chen, Fuwen Luo, Yurui Dong, Yuanchi Zhang, Yuzhuang Xu, Xiaolong Wang, Peng Li, Yang Liu
| Challenge: | Existing benchmarks for evaluating MLLMs have not addressed active perception . a novel benchmark is proposed to evaluate active perception in ML models . |
| Approach: | They propose a benchmark to evaluate active perception in Multimodal Large Language Models . they restrict the perceptual field of a model and require it to actively zoom or shift it . |
| Outcome: | The proposed benchmark focuses on a specialized form of Visual Question Answering (VQA) that eases and quantifies the evaluation yet challenging for existing MLLMs. |
LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | Clinical trials are costly and pivotal processes that require substantial expenses . a new approach to integrate multimodal data for clinical outcome prediction is needed . |
| Approach: | a proposed framework transforms modality-specific data into natural language descriptions . a sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities . |
| Outcome: | a proposed framework outperforms baseline methods in predicting clinical trial outcomes . it transforms modality-specific data into natural language descriptions, encoded via unified encoders . |
Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction (2025.naacl-long)
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| Challenge: | Grammatical error correction (GEC) aims to correct grammatical, spelling, and semantic errors in natural language text. |
| Approach: | They propose a retrieval method based on natural language grammatical error explanations to match inputs with pre-constructed databases where explanations for erroneous samples are generated by LLMs. |
| Outcome: | The proposed method outperforms existing semantic and BM25-based retrieval techniques without additional training or language adaptation. |
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws (2024.emnlp-main)
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| Challenge: | Existing quality filtering methods rely on a high-quality dataset as reference . Existing methods introduce potential biases and compromise diversity . |
| Approach: | They propose a method that evaluates text quality based on the perplexity difference between two language models trained on the same data. |
| Outcome: | The proposed approach improves performance of pre-trained models without increasing training costs. |
Adaptive Hyper-parameter Learning for Deep Semantic Retrieval (2023.emnlp-industry)
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| Challenge: | Existing methods for deep semantic retrieval are highly sensitive to hyper-parameters . a novel adaptive metric learning method is proposed to overcome this limitation . |
| Approach: | They propose a method that adaptively obtains hyper-parameters without fixed or extra-trainable hyper-parmeters . they adopt a symmetric metric learning method to mitigate model collapse issues . |
| Outcome: | The proposed method outperforms existing methods on a real-world dataset and brings economic benefits. |
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation (2024.acl-long)
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Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng
| Challenge: | Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts. |
| Approach: | They have developed an unconstrained hallucination generation evaluation benchmark that contains hallucines generated by large language models with minimal restrictions. |
| Outcome: | The proposed benchmarks are based on a Chinese-language dataset that is lacking in the field. |
OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding (2023.emnlp-demo)
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| Challenge: | Event understanding is fundamental for humans to understand the world. |
| Approach: | They propose an event understanding toolkit called OmniEvent that is comprehensive and fair . it supports mainstream modeling paradigms and the processing of 15 widely-used datasets . |
| Outcome: | The toolkit supports mainstream modeling paradigms and the processing of 15 widely-used English and Chinese datasets. |
Contrastive Preference Learning for Neural Machine Translation (2024.findings-naacl)
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| Challenge: | Existing discrepancies between token-level objective and overall sequence-level quality of a model are causing exposure bias and other issues in NMT. |
| Approach: | They propose a contrastive preference model that integrates an indicator function to fine-tune a pre-trained model in Neural Machine Translation. |
| Outcome: | The proposed model outperforms the traditional Plackett-Luce model on three language pairs and also outperFORMs token-level and sequence-level baseline models. |
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression (2025.findings-naacl)
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| Challenge: | Existing approaches to augment language models with external knowledge but they are limited by static nature of pre-training data. |
| Approach: | They propose a lightweight approach that compresses retrieved documents into highly dense textual summaries to integrate into in-context RAG. |
| Outcome: | The proposed approach reduces latency and costs while achieving high performance in open-domain questions. |
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories (2024.findings-emnlp)
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Yifan Song, Weimin Xiong, Xiutian Zhao, Dawei Zhu, Wenhao Wu, Ke Wang, Cheng Li, Wei Peng, Sujian Li
| Challenge: | Existing studies focus on specialized agents designed for particular tasks. |
| Approach: | They propose to scale annotated interaction trajectories and fine-tune LLMs on AgentBank to get a series of agent models, Samoyed. |
| Outcome: | The proposed model can scale to get generalized agent capabilities. |
DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models (2024.findings-naacl)
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| Challenge: | Encoder-decoder transformer models suffer from high inference latency due to auto-regressive decoding . Typically, the decoder takes up most of the latency because of the auto-decoding - a problem that is not solved by the current model. |
| Approach: | They propose an approach to perform Dynamic Early Exit on Decoder to reduce inference latency by 20%-74% by using a multi-exit encoder-decoder transformer model trained with deep supervision. |
| Outcome: | The proposed model reduces inference latency by 20%-74% with comparable or even higher accuracy compared to baseline models. |
Budget-Constrained Tool Learning with Planning (2024.findings-acl)
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| Challenge: | Existing methods for budget-constrained tool learning have been overlooked . et al., 2023b) compared tool learning with other methods to improve performance . |
| Approach: | They propose a method for budget-constrained tool learning by creating a preferable plan under the budget constraint before utilizing the tools. |
| Outcome: | The proposed method reduces the cost of tool learning and reaches competitive Pass Rate. |
MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)
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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu
| Challenge: | Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets. |
| Approach: | They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets. |
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)
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Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu
| Challenge: | Existing frameworks for Augmented Language Models lack flexibility, democratization, and holistic evaluation. |
| Approach: | They propose a lightweight and extensible framework for Augmented Language Models called Gentopia. |
| Outcome: | The proposed framework integrates language models, task formats, prompting modules, and plugins into a unified paradigm. |
Hypoformer: Hybrid Decomposition Transformer for Edge-friendly Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to compress Transformer are limited to sub-components, e.g., selfattention networks or embedding layer. |
| Approach: | They propose a Hybrid Tensor-Train decomposition which retains full rank and meanwhile reduces operations and parameters. |
| Outcome: | The proposed model outperforms light-weight SOTA methods on three translation tasks and achieves 7.1 points absolute improvement in BLEU and 1.27 X speedup on IWSLT’14 De-En task. |
Investigating the Multilingual Calibration Effects of Language Model Instruction Tuning (2026.eacl-short)
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Jerry Huang, Peng Lu, Qiuhao Zeng, Yusuke Iwasawa, Yutaka Matsuo, Sarath Chandar, Edison Marrese-Taylor, Irene Li
| Challenge: | despite advances in foundation model research, the relationship between large language models and their calibration remains an open area of research. |
| Approach: | They examine a gap in the calibration of large language models within multilingual settings to better understand how data scarcity can potentially lead to different calibration effects. |
| Outcome: | The proposed calibration gap is found in two multilingual benchmarks over 29 and 42 languages. |
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)
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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye
| Challenge: | Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences. |
| Approach: | They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks . |
| Outcome: | The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency. |
LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment (2026.findings-acl)
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Shipeng Li, Zhiqin Yang, Shikun Li, Xiaobo Xia, Hengyu Liu, Xinghua Zhang, Gaode Chen, Dong Fang, Ying Tai, Zhe Peng
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) is a key technique for enhancing LLMs’ reasoning abilities, yet its data inefficiency remains a major bottleneck. |
| Approach: | They propose a gradient-alignment-based method which intelligently selects the learnable and representative training reasoning data for RLVR post-training. |
| Outcome: | Experiments on five reasoning benchmarks show that the proposed method significantly reduces training data requirements while improving performance. |
Coreferential Reasoning Learning for Language Representation (2020.emnlp-main)
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| Challenge: | Existing language representation models cannot explicitly handle coreference, which is essential to the coherent understanding of the whole discourse. |
| Approach: | They propose a language representation model that captures coreferential relations in context. |
| Outcome: | The proposed model can achieve significant improvements on downstream NLP tasks while maintaining comparable performance to baseline models on other common NLP task. |
Packed Levitated Marker for Entity and Relation Extraction (2022.acl-long)
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| Challenge: | Existing work on entity and relation extraction ignores the interrelation between spans . a novel approach to extract better span representations from pre-trained languages is needed . |
| Approach: | They propose a span representation approach that packs Levitated Markers to consider interrelation between spans. |
| Outcome: | The proposed model improves on baselines on six NER benchmarks and achieves a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models. |
On the Consistency of Commonsense in Large Language Models (2025.findings-acl)
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| Challenge: | Existing evaluations of commonsense for large language models focus on downstream knowledge tasks, failing to probe whether LLMs truly understand and utilize knowledge or merely memorize it. |
| Approach: | They propose to automatically construct a large benchmark named CoCo which measures LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. |
| Outcome: | The proposed benchmark systematically assesses LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. |
Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)
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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available. |
| Approach: | They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs. |
| Outcome: | The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer. |
Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)
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| Challenge: | Existing approaches to reading comprehension systems are vulnerable to adversarial attacks. |
| Approach: | They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model. |
| Outcome: | The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks. |
Acquisition and Application of Novel Knowledge in Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for constructing new datasets rely on timestamps or simple templates that do not accurately reflect the real world. |
| Approach: | They propose a knowledge dataset construction approach that simulates biological evolution using knowledge graphs to generate synthetic entities with diverse attributes. |
| Outcome: | The proposed framework outperforms knowledge augmentation methods by 3.3%-38%. |
MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)
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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou
| Challenge: | Existing datasets exhibit data scarcity and limited coverage of general-domain events. |
| Approach: | They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types. |
| Outcome: | The proposed dataset shows that existing methods cannot achieve promising results on the small datasets. |
G2: Guided Generation for Enhanced Output Diversity in LLMs (2025.emnlp-main)
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| Challenge: | Existing approaches to enhance output diversity but compromise quality of outputs. |
| Approach: | They propose a training-free plug-and-play method that enhances output diversity while preserving generation quality. |
| Outcome: | The proposed method enhances output diversity while maintaining an optimal balance between diversity and quality. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
A Mixture of h - 1 Heads is Better than h Heads (2020.acl-main)
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| Challenge: | Evidence has shown that multi-head attentive neural architectures are overparameterized. |
| Approach: | They propose a multi-head attentive neural architecture that “reallocates” attention heads to different inputs. |
| Outcome: | The proposed model outperforms baselines on machine translation and language modeling tasks. |
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (2021.emnlp-main)
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| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
| Approach: | They propose a personalized topic attention model that captures salient contents to personalize hashtag contexts by predicting how likely a user will post with a hashtag. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. |
LightFormer: Light-weight Transformer Using SVD-based Weight Transfer and Parameter Sharing (2023.findings-acl)
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| Challenge: | Deploying Transformer networks on resource-constrained edge devices is challenging. |
| Approach: | They propose a low-rank factorization initialized by SVD-based weight transfer and parameter sharing to compress and accelerate Transformer networks. |
| Outcome: | The proposed method achieves similar performance to the baseline Transformer with 3.8 times and 1.8 times fewer parameters and achieves 2.3 times speedup and 1.5 times speed up respectively. |
ZSEE: A Dataset based on Zeolite Synthesis Event Extraction for Automated Synthesis Platform (2024.findings-naacl)
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| Challenge: | Automated synthesis of zeolite holds great significance for attaining economic and environmental benefits. |
| Approach: | They propose an event extraction task to mine structural synthesis actions from experimental narratives for modular automated synthesis. |
| Outcome: | The proposed method can significantly expedite automated synthesis of zeolites owing to its machine readability. |
Perspective Transition of Large Language Models for Solving Subjective Tasks (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing . performance of LLMs on subjective tasks is limited, authors say . |
| Approach: | They propose a method that allows LLMs to select between direct, role, and third-person perspectives for best way to solve corresponding subjective problem. |
| Outcome: | The proposed method outperforms widely used single fixed perspective based methods on 12 subjective tasks. |
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge. |
| Approach: | They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts. |
| Outcome: | The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts. |
Revisiting Large Language Models as Zero-shot Relation Extractors (2023.findings-emnlp)
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| Challenge: | Recent studies show that large language models (LLMs) transfer well to new tasks out-of-the-box . relationship extraction (RE) involves a certain degree of labeled or unlabeled data even under zero-shot setting. |
| Approach: | They propose a simple prompt recursively using LLMs to transform RE inputs to QA format . they propose qq prompting and qt prompting to improve their results . |
| Outcome: | The proposed method improves on different model sizes, benchmarks and settings. |
Towards Fine-grained Text Sentiment Transfer (P19-1)
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| Challenge: | Existing methods for fine-grained text sentiment transfer only reverse the sentiment polarity of text, but they lack a robust and parallel learning algorithm. |
| Approach: | They propose a novel fine-grained text sentiment transfer task that revises a sequence to satisfy a given sentiment intensity while preserving the original semantic content. |
| Outcome: | The proposed model outperforms existing methods by a large margin in automatic evaluation and human evaluation. |
Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge (2025.acl-long)
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| Challenge: | Existing methods for hyperbole and metaphor detection focus on superficial text features, ignoring the associations of hyperbola and metaphor . Existing frameworks focus on identifying superficial text, focusing on superficial features . |
| Approach: | They propose an emotion-guided hyperbole and metaphor detection framework based on bidirectional dynamic interaction. |
| Outcome: | The proposed framework outperforms baseline methods on four datasets. |
Be Helpful but Don’t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support (2024.emnlp-main)
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| Challenge: | a helpful speaker should maintain an "effect-effort" tradeoff for a conversation to help and support . a study aimed to cultivate the awareness of "optimal relevance" into the cognitive process of conversation agents . |
| Approach: | They integrate the "Cognitive Relevance Principle" into emotional support agents . they found that the "relevance principle" is effective in generating human-like, helpful, harmless conversations . |
| Outcome: | The proposed method improves human-likedness and support in multi-turn conversations . the source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git . |
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| Challenge: | Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically . |
| Approach: | They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE . |
| Outcome: | The proposed methods can extract relational facts from text, but they are still lacking in the current field. |
FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension (2024.emnlp-main)
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| Challenge: | Referring Expression Comprehension (REC) is a cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding. |
| Approach: | They propose to use a new reference expression comprehension (REC) dataset to evaluate the capabilities of language understanding, image comprehension, and language-to-image grounding. |
| Outcome: | The proposed model is able to reject scenarios where the target object is not visible in the image, a key aspect often overlooked in existing models and approaches. |
Intrinsic Mutual Information as a Modulator for Preference Optimization (2026.findings-acl)
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| Challenge: | Existing methods for offline preference optimization involve additional hyperparameter tuning, resulting in substantial time overhead. |
| Approach: | They propose a lightweight framework for offline preference optimization that leverages hyperparameter modulation to decouple preference contributions. |
| Outcome: | The proposed framework achieves superior performance over existing methods while reducing training overhead by more than 15%. |
Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training (2026.acl-long)
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| Challenge: | Existing methods for reweighting data mixtures rely on manual designation with certain heuristics based on intuition or empirical results. |
| Approach: | They propose a model-based framework that learns to re-weight domains by reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment. |
| Outcome: | The proposed framework outperforms baselines in achieving balanced performance across source and target fields and domain spaces without retraining. |
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning (2025.emnlp-main)
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| Challenge: | Existing approaches to improve mathematical reasoning require extensive datasets for training or depend on few-shot methods that compromise computational accuracy. |
| Approach: | They propose a training-free adaptation framework that efficiently equips general-purpose pre-trained language models with enhanced mathematical reasoning capabilities. |
| Outcome: | The proposed framework outperforms Qwen2.5-72B-Math-Instruct on MMLU-STEM with a score of 90.9%, compared to 87.3%. |
Adversarial Training for Weakly Supervised Event Detection (N19-1)
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| Challenge: | Detecting and identifying events is an important subtask of event extraction. |
| Approach: | They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods on two real-world datasets. |
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning (2026.acl-long)
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Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Fei Huang, Ya-Qin Zhang, Yang Liu
| Challenge: | Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited. |
| Approach: | They propose an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT. |
| Outcome: | Experiments on 7B-scale writer models show that Writing-RL improves long-form writing performance over strong SFT baselines. |
SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks (2026.acl-long)
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Tianyi Wang, Yixia Li, Long Li, Yibiao Chen, Shaohan Huang, Yun Chen, Peng Li, Yang Liu, Guanhua Chen
| Challenge: | Proximal Policy Optimization (PPO) is central to aligning Large Language Models with verifiable rewards. |
| Approach: | They propose a scalable algorithm that harmonizes sample efficiency with stability of outcome-based updates. |
| Outcome: | The proposed algorithm outperforms standard PPO and matches the performance of computation-heavy group-based methods. |
Can Large Language Models Be Good Language Teachers? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse domains, but their potential as effective language teachers remains inadequately assessed. |
| Approach: | They propose a framework to evaluate Chinese language teachers' pedagogical competence against international standards. |
| Outcome: | The proposed framework evaluates 13 latest multilingual and Chinese LLMs against international standards for Chinese language teachers. |
Psychology-guided Controllable Story Generation (2022.coling-1)
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| Challenge: | Existing controllable story generation systems ignore the psychological changes of the protagonists and focus on the appointed keywords or emotions. |
| Approach: | They propose a Psychology-guided Controllable Story Generation System (PICS) that generates stories that adhere to the given leading context and desired psychological state chains for the protagonist. |
| Outcome: | The proposed system outperforms baselines and shows that it can generate stories with more consistent psychological changes. |
New Datasets and Controllable Iterative Data Augmentation Method for Code-switching ASR Error Correction (2023.findings-emnlp)
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| Challenge: | In bilingual or multilingual settings, code-switching ASR has greater challenges and research value. |
| Approach: | They propose a controllable iterative method for improving the performance of mainstream automatic speech recognition systems by using Chinese-English code-switching dialogues. |
| Outcome: | The proposed method achieves the best performance compared with the rule-based, back-translation-based data augmentation methods and large language model ChatGPT. |
BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language? (2024.findings-emnlp)
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| Challenge: | Dense retrieval systems focus on optimizing text embedding space while overlooking Boolean logic in language. |
| Approach: | They propose a task to investigate whether retrieval systems can comprehend Boolean logic in language. |
| Outcome: | The proposed method is based on a benchmark dataset covering complex queries containing basic Boolean logic and corresponding annotated passages. |
QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization (2026.acl-long)
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Changxin Ke, Rui Zhang, Jiaming Guo, Yuanbo Wen, Li Ding, Shuo Wang, Xuyuan Zhu, Xiong Peng, Di Huang, Zidong Du, Xing Hu, Qi Guo, Yunji Chen
| Challenge: | Existing approaches to program repair are based on correctness alone. |
| Approach: | They propose a framework that mitigates over-editing and improves repair accuracy by generating buggy programs and re-edits. |
| Outcome: | The proposed framework improves repair precision by 31.4% under fix1@1, a metric that considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing. |
Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA (2024.acl-long)
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| Challenge: | Recent research focuses on improving prediction performance and reliability of LLM. |
| Approach: | They propose a method to actively extract valuable information from the knowledge to produce a latent vector as a soft prompt, which is fused with the image embedding to form a knowledge-enhanced context to instruct LLM. |
| Outcome: | The proposed method improves performance on knowledge-based VQA benchmarks. |
Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)
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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone. |
| Approach: | They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs. |
| Outcome: | The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively. |
Rethinking Depression Prediction from a Fine-Grained Subscore Modeling Perspective via Multi-Task Learning (2026.acl-long)
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| Challenge: | Existing methods for depression assessment rely on standardized ratings, but they are time-consuming and subject to inter-rater variability. |
| Approach: | They propose a fine-grained model for subscore prediction via multi-task learning that can be used to predict depression severity using multiple tasks. |
| Outcome: | The proposed model outperforms baselines and Qwen3-14B direct scoring on the public E-DAIC dataset and to a large-scale private clinical dataset. |
Don’t Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination (2026.acl-industry)
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Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit, Jiaxin Zhang, Vaibhav Vats, Yu Li, Xiangyu Peng, Chien-Sheng Wu
| Challenge: | Enterprise deep research systems fail to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. |
| Approach: | They propose a scalable Enterprise Deep Research (EDR) architecture that decomposes requests into coverage-driven objectives via outline generation with reflection and localizes context with dependency-guided execution and explicit information sharing. |
| Outcome: | The proposed system achieves the strongest overall performance compared with competitive deep-research baselines on internal sales enablement tasks and the public DeepResearch Bench benchmark. |
WildReward: Learning Reward Models from In-the-Wild Human Interactions (2026.acl-long)
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| Challenge: | Prior work focused on collecting preference pairs, requiring substantial annotation efforts. |
| Approach: | They propose a pipeline to extract reliable human feedback from in-the-wild interactions . they propose to use WildChat as an interaction source to train the model . |
| Outcome: | The proposed model achieves comparable or even superior performance compared to conventional models with improved calibration and cross-sample consistency. |
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation (2024.emnlp-main)
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Wenhao Huang, Zhouhong Gu, Chenghao Peng, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Liqian Wen, Zulong Chen
| Challenge: | Existing methods for web scraping suffer from limited adaptability and scalability when faced with a new website. |
| Approach: | They propose a framework that generates web scrapers with large language models and a new executability metric to measure the performance of web scraper generation tasks. |
| Outcome: | The proposed framework can handle diverse web environments more efficiently. |
Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
| Challenge: | Existing methods to build named entity recognition systems with limited labeled data are lacking. |
| Approach: | They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited. |
| Outcome: | The proposed NER systems outperform existing methods on few-shot and training-free settings. |
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text (2020.emnlp-main)
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| Challenge: | MRC has achieved significant progress on the open domain in recent years due to large-scale pre-trained language models. |
| Approach: | They propose a machine reading comprehension model which exploits structural medical knowledge and reference medical plain text to improve the exam's accuracy. |
| Outcome: | The proposed model outperforms existing models with a large margin and passes the exam with 61.8% accuracy rate on the test set. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
Control Large Language Models via Divide and Conquer (2024.emnlp-main)
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| Challenge: | Lexically Constrained Generation (LCG) is a crucial task of text generation. |
| Approach: | They propose a Divide and Conquer Generation strategy to enhance LLMs' performance in Lexically Constrained Generation with prompt-based controlling. |
| Outcome: | The proposed strategy shows 90% improvement on the most challenging LCG task. |
Facilitating Cross-lingual Transfer of Empathy through Language-independent Latent Diffusion: A Case Study in Chinese (2025.findings-emnlp)
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| Challenge: | Existing human empathy data are limited to English . a new study examines the pragmatic transferability of empathy across languages . |
| Approach: | a team of researchers integrate language-independent diffusion processes to facilitate the cross-lingual transfer of empathy. |
| Outcome: | The proposed method demonstrates that empathy can be transferred across languages without compromising linguistic naturalness. |
FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning (2026.acl-long)
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Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Nenkov Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, Preslav Nakov
| Challenge: | Existing benchmarks emphasize final numerical answers while neglecting intermediate reasoning steps. |
| Approach: | They propose a symbolic benchmark for verifiable Chain-of-Thought evaluation in finance . FINCHAIN spans 58 topics across 12 financial domains and three difficulty levels . |
| Outcome: | The proposed benchmark aims to bridge symbolic reasoning and factual verification. |
Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) often experience “contextual hallucination” where they prioritize self-generated content over input context, leading to a disregard for pertinent details. |
| Approach: | They propose a method that dynamically adjusts attention maps to enhance contextual relevance by using a trained classifier to identify attention maps likely to induce hallucinations. |
| Outcome: | The proposed approach reduces hallucinations across open-source models on summarization and open-book QA tasks. |
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022.acl-long)
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| Challenge: | Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. |
| Approach: | They propose an unsupervised reference-free metric which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks. |
| Outcome: | The proposed metric has higher correlations with human judgments while obtaining better generalization of evaluating generated texts from different models and with different qualities. |
RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems (2021.acl-long)
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| Challenge: | Existing task-oriented dialog systems are less than satisfactory in robustness evaluation . existing systems are weak in robustity evaluation based on pre-training and fine-tuning . |
| Approach: | They propose to use a set of training examples to evaluate model generalization ability . they propose to include tasks with limited training data to favor models with strong generalization abilities . |
| Outcome: | The proposed model generalizes well with limited training data and is robust to user input across domains. |
Reason-Code: Reliable Code Generation via Test-Driven Monte Carlo Tree Search (2026.acl-industry)
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| Challenge: | Large Language Models (LLMs) are widely used for code generation, but their performance degrades on complex tasks. |
| Approach: | They propose an inference-time framework that formulates code generation as a search process guided by execution feedback. |
| Outcome: | The proposed framework improves reliability without paying full cost of additional sampling under strict latency budgets. |
Sparrow: Text-Anchored Window Attention with Visual-Semantic Glimpsing for Speculative Decoding in Video LLMs (2026.acl-long)
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| Challenge: | Existing speculative decoding models face performance collapse due to key-value cache explosion and context window mismatches. |
| Approach: | They propose a framework that offloads visual computation to the target model by using hidden state reuse. |
| Outcome: | The proposed framework achieves an average speedup of 2.82x even with 25k visual tokens . |
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference (2025.naacl-long)
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Zeping Li, Xinlong Yang, Ziheng Gao, Ji Liu, Guanchen Li, Zhuang Liu, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum
| Challenge: | Existing methods such as Medusa lack adequate information interaction between different drafting heads. |
| Approach: | They propose an enhanced speculative decoding framework that builds upon Medusa and integrates a drafting block capable of parallel inference. |
| Outcome: | The proposed framework outperforms Medusa in terms of head accuracy and latency. |
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)
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Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian
| Challenge: | Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic. |
| Approach: | They propose a chain-of-thought reasoning framework with three key designs to address these issues. |
| Outcome: | The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG. |
MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues (2026.findings-acl)
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Yaning Pan, Qianqian Xie, Guohui Zhang, Zekun Moore Wang, Yongqian Wen, Yuanxing Zhang, Haoxuan Hu, Zhiyu Pan, Yibing Huang, Zhidong Gan, Yonghong Lin, An Ping, Shihao Li, Yanghai Wang, Tianhao Peng, Jiaheng Liu
| Challenge: | Existing evaluation benchmarks for Multimodal Large Language Models (MLLMs) focus on single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios. |
| Approach: | They propose a video understanding benchmark for MLLMs in multi-turn dialogues that assesses six core competencies that focus on perceptivity and interactivity. |
| Outcome: | The MT-Video-Bench evaluates 1,000 multi-turn dialogues from diverse domains and reveals significant performance discrepancies and limitations in handling multi-turned video dialogues. |
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)
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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps. |
| Approach: | They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts. |
| Outcome: | The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models. |
Mitigating Language Bias of LMMs in Social Intelligence Understanding with Virtual Counterfactual Calibration (2024.emnlp-main)
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| Challenge: | Existing work on social intelligence using large multimodal models is under-explored due to the prevalence of text-based data in the pretraining stage. |
| Approach: | They propose a structure causal model to mitigate the negative language biases of large multimodal models by preserving beneficial priors. |
| Outcome: | The proposed model minimizes negative language bias while preserving beneficial priors while avoiding spurious correlations between LMMs' internal commonsense knowledge and the given context. |
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)
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| Challenge: | Existing approaches to represent knowledge in the low-dimensional space are to leverage large-scale unsupervised text corpus to train fixed or contextual representations. |
| Approach: | They propose to leverage large-scale unsupervised text corpus to train fixed or contextual language representations and to express knowledge into a knowledge graph (KG) they incorporate distributional representations of a KG onto the representations from pre-trained language models, via simply concatenation or multi-head attention. |
| Outcome: | The proposed models outperform the other models on the COIN: COmmonsense INference in Natural Language Processing (COIN) Workshop datasets. |
Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)
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| Challenge: | Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles. |
| Approach: | They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments . |
| Outcome: | The proposed model can achieve comparable results to existing methods on two widely-used datasets. |
FLAG-TRADER: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading (2025.findings-acl)
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Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities in financial tasks, but struggle with multi-step, goal-oriented scenarios in interactive financial markets. |
| Approach: | They propose a framework that integrates large language models with gradient-driven reinforcement learning (RL) policy optimization. |
| Outcome: | The proposed framework improves performance in trading and other financial domain tasks. |
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)
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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks. |
| Approach: | They propose a benchmark to assess the ability of long-context large language models to handle long-text problems. |
| Outcome: | The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint . |
Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs (2026.findings-eacl)
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| Challenge: | Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. |
| Approach: | They propose a variant that operates on a turn-level MDP formulation, instead of the commonly used token-level one. |
| Outcome: | The proposed method is more robust than the widely used GRPO algorithm and more efficient than token-level MDPs. |
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)
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| Challenge: | Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers. |
| Approach: | They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM. |
| Outcome: | The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks. |
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)
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| Challenge: | Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. |
| Approach: | They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks. |
| Outcome: | The proposed model significantly outperforms baselines across the board in e-commerce scenarios. |
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)
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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang
| Challenge: | Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting. |
| Approach: | They propose a representation-aware model merging framework for continual learning without access to historical data. |
| Outcome: | The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios. |
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)
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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu
| Challenge: | Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios. |
| Approach: | They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy. |
| Outcome: | The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em. |
Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)
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| Challenge: | Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles. |
| Approach: | They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments . |
| Outcome: | The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well . |
RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods struggle to reason about related conceptual knowledge effectively, despite a lack of model-level relational reasoning. |
| Approach: | They propose a benchmark to assess concept-level and instance-level relational reasoning abilities of edited models. |
| Outcome: | The proposed model obtains the best scores on the memory-based in-context editing baseline, MICE, suggesting a promising direction for model editing. |
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)
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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao
| Challenge: | Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. |
| Approach: | They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison. |
| Outcome: | The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research. |
DEEM: Dynamic Experienced Expert Modeling for Stance Detection (2024.lrec-main)
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| Challenge: | Existing work on stance detection tasks using large language models shows promising results, but it may not be able to provide detailed background knowledge. |
| Approach: | They propose a method which leverages the generated experienced experts and lets LLMs reason in a semi-parametric way. |
| Outcome: | The proposed method outperforms methods with self-consistency reasoning and reduces bias. |
VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild (2024.acl-long)
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| Challenge: | VoiceCraft is a token-infilling neural codec language model for speech editing and zero-shot text-to-speech evaluation. |
| Approach: | They introduce a token infilling neural codec language model that performs on speech editing and zero-shot text-to-speech tasks. |
| Outcome: | The proposed model outperforms previous models on speech editing and zero-shot text-to-speech tasks. |
PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation (2023.emnlp-main)
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| Challenge: | Pro-drop (‘pronoun-dropping’) language requires NMT systems to recover omitted pronouns, but this task lacks sufficient datasets for benchmarking . |
| Approach: | They propose a benchmarking method that leverages the semantic embedding of dropped pronouns to augment training pairs to alleviate the negative impact introduced by pro-drop . |
| Outcome: | The proposed method outperforms existing methods regarding omitted pronoun retrieval and overall translation quality on four Chinese-English translation corpora. |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
| Approach: | They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
| Outcome: | The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025.acl-long)
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Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, K.p. Subbalakshmi, Jimin Huang, Lingfei Qian, Xueqing Peng, Jordan W. Suchow, Qianqian Xie
| Challenge: | Recent advances have underscored the potential of large language model (LLM)-based agents in financial decision-making. |
| Approach: | They propose to evaluate LLM agents using 13 different LLMs as backbone models across various market environments and tasks. |
| Outcome: | The proposed framework assesses the reasoning and decision-making capabilities of 13 different LLMs across various market environments and tasks. |
MKT: A Multi-Stage Knowledge Transfer Framework to Mitigate Catastrophic Forgetting in Multi-Domain Chinese Spelling Correction (2025.emnlp-industry)
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Peng Xing, Yinghui Li, Shirong Ma, Xinnian Liang, Haojing Huang, Yangning Li, Shu-Yu Guo, Hai-Tao Zheng, Wenhao Jiang, Ying Shen
| Challenge: | Chinese Spelling Correction (CSC) is a model that detects and corrects spelling errors in given sentences. |
| Approach: | They propose a model-agnostic model with an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain rather than focusing solely on new domain knowledge. |
| Outcome: | The proposed model-agnostic framework is based on an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain, rather than focusing solely on new domain knowledge. |
Odysseus Navigates the Sirens’ Song: Dynamic Focus Decoding for Factual and Diverse Open-Ended Text Generation (2025.acl-long)
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| Challenge: | Existing decoding methods struggle to balance factuality and diversity . Deterministic decoding approaches suffer from degeneration and lack of diversity - a problem that is not addressed by the current literature. |
| Approach: | They propose a plug-and-play stochastic approach that adjusts decoding focus based on distributional differences across layers, leveraging the modular nature of factual knowledge within LLMs. |
| Outcome: | Extensive experiments on seven datasets show that DFD significantly improves performance. |
ORANGE: Text-video Retrieval via Watch-time-aware Heterogeneous Graph Contrastive Learning (2023.emnlp-industry)
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Yucheng Lin, Tim Chang, Yaning Chang, Jianqiang Ma, Donghui Li, Ting Peng, Zang Li, Zhiyi Zhou, Feng Wang
| Challenge: | Existing methods for text-video retrieval focus on informative representations and delicate matching mechanisms, but real-world scenarios often involve brief, ambiguous queries and low-quality videos. |
| Approach: | They propose a novel method to learn informative embeddings for queries and videos . they use a watch-time-aware contrastive learning paradigm to capture dependencies . |
| Outcome: | The proposed method is effective in a real-world video-search service. |
Disentangle-based Continual Graph Representation Learning (2020.emnlp-main)
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| Challenge: | Existing graph embedding methods overlook streaming nature of incoming data in real-world applications. |
| Approach: | They propose a disentangle-based continual graph representation learning framework inspired by the human’s ability to learn procedural knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art continual graph representation learning framework and alleviate catastrophic forgetting problem. |
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation (2024.findings-emnlp)
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| Challenge: | Current LLMs are achieving better performance on various benchmarks, but their performance in practical applications does not always match their benchmark results. |
| Approach: | They propose to detect and rewrite leaked benchmarks without altering their difficulties by using Inference-Time Decontamination (ITD) to mitigate performance inflation caused by memorizing leaked samples. |
| Outcome: | The proposed method reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU. |
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge (2021.findings-emnlp)
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| Challenge: | Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. |
| Approach: | They propose a pSychological-Knowledge-Aware Interaction Graph to model the emotional state of an utterance in the context of a conversation. |
| Outcome: | The proposed method achieves state-of-the-art and competitive performance on four popular CER datasets. |
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents (2025.acl-long)
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Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong
| Challenge: | Existing models that use Large Language Models (LLMs) show superior performance in various tasks, but lack of controllability leads to unfocused conversations or task failure. |
| Approach: | They propose a standard operating procedure (SOP) framework to regulate dialogue flow by integrating Chain of Thought reasoning and supervised fine-tuning for SOP prediction. |
| Outcome: | The proposed method achieves a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also shows notable gains for open-source models. |
REFFLY: Melody-Constrained Lyrics Editing Model (2025.naacl-long)
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| Challenge: | Automatic melody-to-lyric (M2L) generation aims to create lyrics that align with a given melody. |
| Approach: | They propose a framework for automatic melody-to-lyric generation that allows for a more flexible approach to creating lyrics from plain text. |
| Outcome: | The proposed framework outperforms baselines Lyra and GPT-4 in musicality and text quality. |
ReFT: Reasoning with Reinforced Fine-Tuning (2024.acl-long)
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| Challenge: | Existing approaches to improve the generalization of large language models are using Supervised Fine-Tuning (SFT) this approach does not show sufficient generalization ability because it only relies on the given CoT data. |
| Approach: | They propose to use Chain-of-Thought annotations to train Large Language Models using supervised fine-tuning to improve generalization. |
| Outcome: | The proposed approach outperforms SFT on GSM8K, MathQA, and SVAMP datasets and shows a superior generalization ability. |
A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)
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| Challenge: | Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival. |
| Approach: | They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews. |
| Outcome: | The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews. |
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction (2023.findings-acl)
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| Challenge: | Existing methods for multi-modal relation extraction lack useful visual information. |
| Approach: | They propose a novel multi-modal relation extraction framework to capture deeper correlations of text, entity pair, and image/objects. |
| Outcome: | The proposed framework captures the deeper correlations of text, entity pair, and image/objects, and extracts useful information. |
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning (P18-1)
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| Challenge: | Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. |
| Approach: | They propose a framework that integrates planning for task-completion dialogue policy learning into a dialogue agent using a world model to mimic real user response and generate simulated experience. |
| Outcome: | The proposed framework integrates planning for task-completion dialogue policy learning with real user interaction and simulated user behavior. |
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)
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| Challenge: | Existing models for language understanding and understanding can be trained to provide contextualized representations of words based on text data. |
| Approach: | They propose a large-scale language VAE model Optimus that is pre-trained on large text corpus and fine-tuned for various language generation and understanding tasks. |
| Outcome: | The proposed model achieves new state-of-the-art on VAE language modeling benchmarks. |
Conversation Learner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems (2020.acl-demos)
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Swadheen Shukla, Lars Liden, Shahin Shayandeh, Eslam Kamal, Jinchao Li, Matt Mazzola, Thomas Park, Baolin Peng, Jianfeng Gao
| Challenge: | a wide variety of tasks have created a need for flexible task-oriented dialog systems . dialog flows are intuitively interpretable but lack the flexibility needed to handle complex dialogs . |
| Approach: | They propose a machine teaching tool for building dialog managers using familiar tools . they convert the dialog flow into a parametric model and use user-system dialog logs as training data . |
| Outcome: | The proposed tool combines the best of both approaches to build dialog managers . it converts the dialog flow into a parametric model and improves it over time . |
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025.coling-main)
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| Challenge: | Existing prompting techniques for Large Multi-Modal Models (LMMs) focus on improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination. |
| Approach: | They propose a prompting scheme that scaffolds coordinates to promote vision-language coordination in Large Multi-Modal Models (LMMs) they overlay a dot matrix within the image as visual information anchors and leverage multi-dimensional coordinates as textual positional references. |
| Outcome: | Experiments on a wide range of vision-language tasks show the superiority of SCAFFOLD prompting over the textual Chain-of-Thought prompting. |
Scientific Discourse Tagging for Evidence Extraction (2021.eacl-main)
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| Challenge: | Primary experimental articles provide the crucial raw material for all subsequent scientific research, but the growing number of scientific literature makes it difficult for domain experts to efficiently utilize them. |
| Approach: | They propose to automatically extract text fragments from primary research papers that describe the evidence presented in that paper's figures and to use them to build models of scientific argument. |
| Outcome: | The proposed method is able to extract text fragments from primary research papers that describe the evidence presented in that paper's figures, and it is transferable to new datasets. |
DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path (2025.findings-emnlp)
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| Challenge: | Existing knowledge graph methods lack adaptability in knowledge-intensive tasks with multiple entities and implicit multi-hop relations. |
| Approach: | They propose a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. |
| Outcome: | DS-MHP outperforms baselines and state-of-the-art methods on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning. |
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, making them increasingly integral to various applications. |
| Approach: | They establish a benchmark to evaluate the robustness of instruction-following LLMs against prompt injection attacks, assessing their ability to discern which instructions to follow and which to disregard. |
| Outcome: | The proposed model is overly sensitive to prompt injection attacks, focusing on the latter part of the prompt without fully understanding the context. |
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning (2022.findings-emnlp)
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| Challenge: | Existing methods to automate story generation focus on single-character stories and lack basiccommonsense reasoning. |
| Approach: | They propose a commonsense-inference Augmentedneural StoryTelling framework that introduces commonsensical reasoning into the story generation process. |
| Outcome: | The proposed method produces significantly more coherent, on-topic, enjoyable andfluent stories than existing models in both the single-character and two-character settings. |
From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)
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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu
| Challenge: | Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning. |
| Approach: | They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model. |
| Outcome: | The proposed method improves egocentric reasoning abilities on six tasks. |
CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
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| Challenge: | Existing EE methods do not model event characteristics from large unsupervised data. |
| Approach: | They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures. |
| Outcome: | The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks. |
VerIF: Verification Engineering for Reinforcement Learning in Instruction Following (2025.emnlp-main)
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| Challenge: | Best practices for RL in instruction following remain underexplored. |
| Approach: | They propose a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model. |
| Outcome: | The proposed method achieves state-of-the-art performance among models of comparable size and generalizes well to unseen constraints. |
When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents? (2026.acl-long)
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Yibo Peng, James Song, Lei Li, Xinyu Yang, Mihai Christodorescu, Ravi Mangal, Corina S. Pasareanu, Haizhong Zheng, Beidi Chen
| Challenge: | Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses on functional correctness. |
| Approach: | They propose to attack functionally correct yet vulnerable (FCV) patches by combining multi-turn reasoning with tool invocation and environment interaction. |
| Outcome: | The proposed FCV-Attack achieves an attack success rate of 40.7% on GPT-5 Mini + OpenHands. |
Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)
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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie
| Challenge: | Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores. |
| Approach: | They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs. |
| Outcome: | The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)
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| Challenge: | Existing studies have explored the use of entity linking (EL) in downstream tasks. |
| Approach: | They propose a modularized entity linking toolkit for easy task adaptation. |
| Outcome: | The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods. |
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks (2023.findings-acl)
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| Challenge: | Recent studies focus on retrieval to solve knowledge-intensive tasks, but the potential of retrieval for non-knowledge-intensive (NKI) tasks remains under-explored. |
| Approach: | They propose a task-agnostic retrieval framework for NKI tasks that uses a static index and a prompt-guided reranker to re-rank the nearest evidence according to task-specific relevance. |
| Outcome: | The proposed framework outperforms state-of-the-art retrieval-augmented methods on NKI tasks and will be released for further research. |
Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)
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| Challenge: | Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from. |
| Approach: | They propose to use LLMs to generate human-like responses by mutability and accessibility of social inputs to perform a social prediction task. |
| Outcome: | The proposed model performs well in three realistic settings and a novel social prediction task. |
How does Misinformation Affect Large Language Model Behaviors and Preferences? (2025.acl-long)
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| Challenge: | Existing studies have explored the role of Large Language Models in combating misinformation, but there is still a lack of detailed analysis on the specific aspects and extent to which LLMs are influenced by misinformation. |
| Approach: | They propose to use a benchmark to evaluate LLMs' behavior and knowledge preference toward misinformation to identify their models. |
| Outcome: | The proposed approach is based on 10,346,712 pieces of misinformation and examines knowledge conflicts and stylistic variations. |
Rethinking Long Context Generation from the Continual Learning Perspective (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) struggle with processing long contexts due to the limited context window. |
| Approach: | They propose to combine a limited context window with a continual learning perspective to improve LLMs' efficiency in processing long contexts. |
| Outcome: | The proposed models improve the performance of Large Language Models (LLMs) by integrating learning strategies with existing approaches. |
End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching (2022.emnlp-main)
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| Challenge: | Existing unsupervised vision-and-language pre-training methods take pre-extracted region-based visual features from external object detectors, which limits flexibility and reduces computational efficiency. |
| Approach: | They propose an unsupervised vision-and-language pre-training task that predicts which patches contain an object referred to in natural language from the encoded visual features. |
| Outcome: | The proposed approach outperforms existing methods and obtains state-of-the-art results on four vision-and-language tasks. |
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models (2026.findings-acl)
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Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Kyle Lam, Bang Zheng, Lin Li, Jianing Qiu
| Challenge: | Existing benchmarks for multimodal large language models do not capture real-world clinical complexity. |
| Approach: | They evaluate multilingual, multimodal multimodal models of clinical cases with up to 7 distinct visual clinical evidence types per case. |
| Outcome: | The proposed model outperforms human models on differential diagnosis (DDx) generation and final diagnosis (FDx) selection. |
Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)
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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in model editing for LLMs have created challenges and opportunities for the community. |
| Approach: | They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
| Outcome: | The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation (2023.acl-long)
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| Challenge: | Multi-aspect controllable text generation has attracted increasing attention . but the mutual interference of multiple prefixes limits its extensibility to training-time unseen combinations. |
| Approach: | They propose to use trainable gates to normalize the intervention of prefixes to restrain the interference. |
| Outcome: | The proposed approach outperforms baselines on constraint accuracy, text quality, and extensibility. |
Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling (2025.emnlp-industry)
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Xiaoyu Liu, Di Liang, Hongyu Shan, Peiyang Liu, Yonghao Liu, Muling Wu, Yuntao Li, Xianjie Wu, Li Miao, Jiangrong Shen, Minlong Peng
| Challenge: | Generative RMs (GRMs) lack contextual and background information during inference, leading to incomplete evaluations. |
| Approach: | They propose a modular and interpretable framework that integrates side-branch models as auxiliary feature generators. |
| Outcome: | The proposed framework outperforms scalar and saline reward models in robustness and alignment with human preferences. |
BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning (2026.findings-acl)
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| Challenge: | Experiments show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
| Approach: | They propose a universal, lightweight compressor that distills relevant evidence from retrieved documents into a concise summary for seamless integration into in-context RAG. |
| Outcome: | Experiments on four open-domain multi-hop question-answering datasets show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)
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| Challenge: | Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models. |
| Approach: | They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain . |
| Outcome: | The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice. |
LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks (2026.acl-srw)
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Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yisi Sang, Yun Wang, Hansu Gu, Peng Zhang, Tun Lu, Toby Jia-Jun Li, Dakuo Wang
| Challenge: | Deploying machine learning models in domain-specific scenarios is challenged by data drift and the scarcity of expert annotations. |
| Approach: | They propose a system that combines an LLM, an AL-assisted compact model and an automatic switch module to assist the active learning process. |
| Outcome: | The proposed system achieves 96–98% switch accuracy and outperforms both models used alone. |
Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)
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| Challenge: | Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge. |
| Approach: | They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality. |
| Outcome: | The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets. |
AprilE: Attention with Pseudo Residual Connection for Knowledge Graph Embedding (2020.coling-main)
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| Challenge: | Existing knowledge graph embedding methods are difficult to model diverse relational patterns, especially symmetric and antisymmetric relations. |
| Approach: | They propose a model which employs triple-level self-attention and pseudo residual connection to model relational patterns. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on public datasets on symmetric and antisymmetric relations. |
Vulnerability of LLMs to Vertically Aligned Text Manipulations (2025.acl-long)
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| Challenge: | Recent research shows that vertical text input significantly degrades the accuracy of large language models (LLMs) in text classification tasks. |
| Approach: | They investigate the impact of vertical text input on the performance of LLMs . they find that chain of thought reasoning does not help LLM recognize vertical input . |
| Outcome: | The proposed model can significantly mislead models, posing a risk of bypassing detection in real-world scenarios involving harmful or sensitive information. |
What Factors Affect LLMs and RLLMs in Financial Question Answering? (2026.findings-acl)
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| Challenge: | Recent studies have focused on large language models and reasoning large language model (RLLMs) however, there are few studies that explore what methods can fully unlock the performance of LLMs and RLLM in the financial domain. |
| Approach: | They examine the effects of prompting methods, agentic frameworks, and multilingual alignment methods on financial question-answering tasks. |
| Outcome: | The results show that prompting methods and agent frameworks improve LLMs' performance . the authors suggest that these frameworks can be used to enhance LLM performance if they are implemented in financial domains. |
Hard Sample Aware Prompt-Tuning (2023.acl-long)
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| Challenge: | Prompt-tuning based few-shot learning has garnered increasing attention in recent years due to its efficiency and promising capability. |
| Approach: | They propose a framework to distinguish informative hard samples from misleading ones in model training. |
| Outcome: | The proposed framework achieves new SOTA results on a series of NLP tasks pushing the SST-5 accuracy to 49.5% (1.1% point absolute improvement), QNLI accuracy to 74.6% (1.9% absolute improvement) |
Few-shot Natural Language Generation for Task-Oriented Dialog (2020.findings-emnlp)
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| Challenge: | Existing methods for NLG depend on heavily annotated data, which is infeasible for new domains. |
| Approach: | They propose a system that converts a dialog act into a response in natural language . they propose 'nuclear language generation' to simulate a few-shot learning setting . |
| Outcome: | The proposed model outperforms existing methods on a large set of annotated datasets. |
LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)
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Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu
| Challenge: | Large language model (LLM) routing assigns each query to the best suitable model from an ensemble. |
| Approach: | They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation . |
| Outcome: | The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing. |
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents (2025.naacl-industry)
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| Challenge: | Existing workflow construction methods require specialized knowledge and task-switching skills. |
| Approach: | They propose a multi-agent workflow framework that incorporates a supervisor, orchestrator, and filler agent. |
| Outcome: | The proposed framework significantly increases the success rate of workflow construction . the proposed framework is based on a dataset of 3,695 real-world business samples . |
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models (2024.emnlp-main)
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| Challenge: | Existing medical large vision language models often generate inaccurate and irrelevant answers that do not align with established medical facts. |
| Approach: | They propose a strategy for controlling factuality risk through calibrated selection of the number of retrieved contexts and a preference dataset to fine-tune the model. |
| Outcome: | The proposed model achieves an average improvement of 20.8% on three medical VQA datasets. |
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)
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| Challenge: | Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency. |
| Approach: | They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities . |
| Outcome: | The proposed framework outperforms the sequence tagging baseline on three benchmark datasets. |
KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models (2025.findings-emnlp)
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| Challenge: | Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. |
| Approach: | They propose a multi-agent framework for structured Socratic teaching with LLMs that integrates a structured SocRule and a consultant-teacher collaborative teaching mechanism. |
| Outcome: | The proposed framework outperforms existing LLMs in natural language generation and dialogue comprehension in the classroom. |
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)
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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou
| Challenge: | Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. |
| Approach: | They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks. |
| Outcome: | The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude. |
PATIMT-Bench: A Multi-Scenario Benchmark for Position-Aware Text Image Machine Translation in Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Current TIMT studies focus on providing translations for all text within an image, neglecting to provide bounding boxes and covering limited scenarios. |
| Approach: | They extend traditional TIMT into position-aware TIMt to support fine-grained translation . they introduce an Adaptive Image OCR Refinement Pipeline to refine results . |
| Outcome: | The proposed model supports fine-grained and layout-preserving translation . the experimental data highlight the scalability and generalizability of the model. |
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts (2022.findings-acl)
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| Challenge: | Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. |
| Approach: | They propose to convert a model into its MoE version with the same parameters and build expert routers to decide which experts will be used for each input. |
| Outcome: | The proposed model can use 10% to 30% of FFN parameters while maintaining over 95% original performance. |
Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration (2026.acl-long)
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| Challenge: | Existing context window extension methods obstruct scaling external knowledge input. |
| Approach: | They develop a multi-agent framework to overcome two core bottlenecks in existing agent orchestration designs. |
| Outcome: | The proposed framework overcomes two core bottlenecks and improves inference-time knowledge integration without longer-context training. |
Unsupervised Knowledge Selection for Dialogue Generation (2021.findings-acl)
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| Challenge: | Existing knowledge selection tasks require the preidentified knowledge to generate informative dialogues. |
| Approach: | They propose a novel method to supervise knowledge selection when the gold knowledge label is unknown by obtaining an oracle knowledge label via distant supervision and leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision. |
| Outcome: | The proposed method outperforms strong supervised baselines on two knowledge-grounded dialogue datasets and generates more informative responses. |
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion (2022.findings-emnlp)
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| Challenge: | Existing work on dependency prior structure integration into pre-trained models is still unclear. |
| Approach: | They propose a dependency-based fusion attention paradigm which explicitly introduces dependency prior structure into pre-trained models and adaptively fuses it with semantic information. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on 10 public datasets, demonstrating the benefits of adaptively fusing dependency structure in semantic matching task. |
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization (2026.acl-long)
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Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang, Zhouliang Yu, Luming Li, Minghao Liu, Yihang Xia, Jiawei Shen, Yuchen Wu, Yixin Cao, Zhaoxiang Zhang, Wenhao Huang, Jiaheng Liu, Ge Zhang
| Challenge: | Existing approaches to formalizing mathematical statements face limitations in accuracy, especially in the context of complex, highlevel problems that involve sophisticated mathematical reasoning. |
| Approach: | They propose a CriticLean framework that elevates the role of the critic from a passive validator to an active learning component and introduce a benchmark to measure models’ ability to distinguish semantically correct from incorrect formalizations. |
| Outcome: | The proposed framework outperforms open- and closed-source benchmarks and shows that it significantly outperformed existing models. |
Emancipating Event Extraction from the Constraints of Long-Tailed Distribution Data Utilizing Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for EE depend on manual annotations, which are expensive and scarce. |
| Approach: | They propose to transform the event extraction task into multi-turn dialogues and a novel method for generating high-quality data. |
| Outcome: | The proposed methods significantly improve existing models’ performance with various paradigms and structures, especially on tail types. |
Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning (2021.emnlp-main)
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| Challenge: | Generally, commonsense knowledge is correlated with culture and geographic locations and is only shared locally. |
| Approach: | They construct a Geo-Diverse Visual Commonsense Reasoning dataset to test vision-and-language models’ ability to understand cultural and geo-location-specific commonsense. |
| Outcome: | The proposed models perform better in non-Western regions including East Asia, South Asia, and Africa than in the Western regions. |
PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training (2022.emnlp-main)
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| Challenge: | Existing studies focus on injecting noises into the input sequence, but feasibility of injecting them into the decoding sequence remains an open question. |
| Approach: | They propose a pre-training paradigm that integrates knowledge-enhanced decoding with noises in the prefix to strengthen the representation learning of entities that span over multiple input tokens. |
| Outcome: | The proposed model achieves state-of-the-art results on two knowledge-driven data-to-text generation tasks with up to 2% BLEU gains. |
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends (2026.findings-acl)
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Yihao Ding, Siwen Luo, Yue Dai, Yanbei Jiang, Zechuan Li, Qiang Sun, Geoffrey Martin, Wei Liu, Yifan Peng
| Challenge: | Visually Rich Document Understanding (VRDU) frameworks are a key area of research . early approaches to VRDU relied on manually crafted rules and domain-specific heuristics . conventional deep learning approaches do not integrate the diverse modalities in documents . |
| Approach: | They review recent advances in MLLM-based Visually Rich Document Understanding (VRDU) their findings highlight emerging trends and promising research directions . |
| Outcome: | The proposed frameworks are scalable, reliable, and adaptable, the authors argue . their findings highlight emerging trends and promising research directions . |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
Unsupervised Dependency Graph Network (2022.acl-long)
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| Challenge: | Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures. |
| Approach: | They propose a model that encourages attention heads to model different dependency relations from raw corpora and a masked language modeling task. |
| Outcome: | The proposed model can induce dependency structures from raw corpora and the masked language modeling task without gold POS tags and any external information. |
Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) have potential for cross-modal understanding . but extending MLLM to handle diverse modalities introduces two challenges . |
| Approach: | They propose a dual-stage compression mechanism to reduce the number of modality tokens per modality and condense it into a single, compact token sequence. |
| Outcome: | Experiments show that Flex-M3 outperforms its counterpart trained on only full-modality data. |
Simplify-Pro: A Two-level and Progressive LLM-based Framework for Auto Long Text Simplification (2026.findings-acl)
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| Challenge: | Existing studies have focused on lexical- and sentence-level simplification, leaving long text simplification comparatively unexplored . |
| Approach: | They propose a two-level and progressive LLM-based framework that establishes an effective paradigm for automatic long text simplification under diverse test scenarios. |
| Outcome: | The proposed framework outperforms advanced and proprietary LLMs in in-domain and out-of-domain simplification tasks and matches or outperformed existing LLM frameworks. |
Tokenization Falling Short: On Subword Robustness in Large Language Models (2024.findings-emnlp)
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| Challenge: | Language models typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary. |
| Approach: | They propose to tokenize raw text into sequences of subword identifiers from a predefined vocabulary . they also investigate the challenges and their impact on large language models . |
| Outcome: | The proposed model can mitigate tokenization issues, but still suffer from typos and other variations. |
FuseGen: PLM Fusion for Data-generation based Zero-shot Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to train Small Task-specific Models (STMs) using synthetic datasets are limited by the low quality of such datasets. |
| Approach: | They propose a data-generation based zero-shot learning framework that uses multiple PLMs to train small task-specific models. |
| Outcome: | The proposed framework outperforms existing methods in boosting performance across tasks. |
FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation (2025.acl-long)
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Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li
| Challenge: | Existing benchmarks focus on standalone programming problems, such as HumanEval, MBPP, and LiveCodeBench. |
| Approach: | They propose to use large language models to evaluate their ability to perform incremental development within code repositories by collecting pull requests from 83 GitHub repositorias and using rule-based and intent-based filtering to construct task instances focused on new feature development. |
| Outcome: | The proposed benchmarks show that large language models perform significantly worse in the FEA-Bench, highlighting considerable challenges in repository-level incremental code development. |
MemCoRL: Alternating Co-Optimization of Memory Retrieval and Utilization via Collaborative Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing research has proposed external memory modules for Large Language Models (LLMs) to overcome the limitations of finite input length and obtain contextual memory beyond the current input. |
| Approach: | They propose a two-stage alternating co-optimization reinforcement learning method that optimizes evidence retrieval and utilization using semantic feedback and rewards. |
| Outcome: | The proposed method outperforms baselines on lexical overlap and semantic similarity metrics, confirming the co-optimization in memory retrieval and memory utilization. |
ADELIE: Aligning Large Language Models on Information Extraction (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) struggle to follow complex instructions of IE tasks due to not being aligned with humans. |
| Approach: | They propose an aligned large language moDEL that effectively solves various IE tasks including closed IE, open IE and on-demand IE. |
| Outcome: | The proposed model achieves state-of-the-art (SoTA) performance among open-source models. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
CODIS: Benchmarking Context-dependent Visual Comprehension for Multimodal Large Language Models (2024.acl-long)
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Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu
| Challenge: | Multimodal large language models have demonstrated promising results in a variety of tasks that combine vision and language. |
| Approach: | They propose a benchmark to assess the ability of models to use contextual information in free-form text to enhance visual comprehension. |
| Outcome: | The proposed model fails to extract and utilize contextual information to improve understanding of images. |
Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)
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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases. |
| Approach: | They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin. |
| Outcome: | The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen. |
On the Impact of Cross-Domain Data on German Language Models (2023.findings-emnlp)
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Amin Dada, Aokun Chen, Cheng Peng, Kaleb Smith, Ahmad Idrissi-Yaghir, Constantin Seibold, Jianning Li, Lars Heiliger, Christoph Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu
| Challenge: | Traditionally, large language models have been trained on general web crawls or domain-specific data. |
| Approach: | They present a German dataset and a dataset aimed at containing high-quality data to examine the importance of data diversity over quality. |
| Outcome: | The proposed model outperforms models trained on quality data on multiple downstream tasks. |
PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents (2026.acl-long)
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Lei Wei, Xiao Peng, null Tt, Guannan Zhang, Chenhao Jiang, Hongyu Li, Lanbo Lin, Yuanwu Xu, Jiayao Liu, Kesu Wang, Bin Wang
| Challenge: | Large Language Model (LLM) agents struggle with ultra-long-horizon tasks requiring hundreds or thousands of interaction steps. |
| Approach: | They propose a framework that reconceptualizes context management as a Next Step Prediction problem. |
| Outcome: | The proposed framework improves task success rates and robust cross-lingual performance. |
A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)
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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li
| Challenge: | Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. |
| Approach: | They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization . |
| Outcome: | The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models . |
Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory (2025.acl-long)
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| Challenge: | Semiparametric language models (LMs) use static storage, which lacks learning capability and is disconnected from the internal information flow of the parametric models. |
| Approach: | They reconceptualize the non-parametric memory represented by kNN-LM as a learnable Mixture-of-Neighbors Induction Memory (MoNIM) this synergizes the induction capabilities of attention heads with the memorization strength of feed-forward networks . |
| Outcome: | The proposed model is a learnable Mixture-of-neighbors induction memory (MoNIM) it synergizes the induction capabilities of attention heads with the memorization strength of feed-forward networks (FFNs). |
Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models (2025.coling-main)
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| Challenge: | Symbols are used in abstract reasoning, chemical property prediction, and tabular question-answering. |
| Approach: | They propose a method that converts symbols to language-based representations to improve their accuracy. |
| Outcome: | The proposed method improves the accuracy of symbols in language-based models. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)
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| Challenge: | Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts. |
| Approach: | They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls. |
| Outcome: | The results show that the evaluations are reliable and lack pipeline evaluations. |
Continual Knowledge Distillation for Neural Machine Translation (2023.acl-long)
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| Challenge: | Current parallel corpora are not publicly accessible but trained models are more readily available. |
| Approach: | They propose a method to take advantage of existing translation models to improve one model of interest. |
| Outcome: | The proposed method improves on Chinese-English and German-English datasets and is robust to malicious models. |
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models (2021.emnlp-main)
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| Challenge: | Backdoor attacks are a serious threat to the safety of reusing deep neural networks (DNNs). |
| Approach: | They propose an efficient online defense mechanism based on robustness-aware perturbations to distinguish poisoned and clean samples to defend against backdoor attacks on natural language processing models. |
| Outcome: | The proposed method achieves better defending performance and lower computational costs than existing defense methods. |
Pluggable Neural Machine Translation Models via Memory-augmented Adapters (2024.lrec-main)
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| Challenge: | Recent years, neural machine translation systems are often developed with large-scale parallel data extracted from the Web. |
| Approach: | They propose a memory-augmented adapter to steer pretrained neural machine translation models in a pluggable manner by combining model representations and retrieved results. |
| Outcome: | The proposed method outperforms several representative pluggable baselines on style- and domain-specific experiments. |
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models (2022.acl-short)
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| Challenge: | Existing pre-trained language models cannot recall factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. |
| Approach: | They propose to build a pluggable Entity Lookup Table (PELT) on demand by aggregating the entity’s output representations of multiple occurrences in the corpora. |
| Outcome: | The proposed model can transfer entity knowledge from out-of-domain corpora into PLMs with different architectures. |
QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm (2025.findings-acl)
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Qirui Zhou, Shaohui Peng, Weiqiang Xiong, Haixin Chen, Yuanbo Wen, Haochen Li, Ling Li, Qi Guo, Yongwei Zhao, Ke Gao, Ruizhi Chen, Yanjun Wu, Zhao Chen, Yunji Chen
| Challenge: | Existing LLMs cannot comprehend the complex data flow and computation process of the attention operator and utilize low-level primitive to exploit GPU performance. |
| Approach: | They propose an LLM-friendly Thinking Language (LLM-TL) that can decouple the generation of high-level optimization logic and low-level implementation on GPU and enhance LLMs’ understanding of attention operator. |
| Outcome: | The proposed method outshines existing LLMs on A100, RTX8000, and T4 GPUs, achieving a speed-up of up to 35.16. |
Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding (2025.emnlp-main)
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| Challenge: | Recent studies have successfully decoded natural language from non-invasive brain signals . current dataset splitting methods suffer from data leakage problem . |
| Approach: | They propose a right cross-subject data splitting criterion without data leakage for decoding fMRI and EEG signal to text. |
| Outcome: | The proposed method overfits and overestimates brain-to-text decoding models. |
Transforming Visual Scene Graphs to Image Captions (2023.acl-long)
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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
| Challenge: | Existing approaches to generate captions using image captioning are based on multi-head attention (MHA) |
| Approach: | They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words. |
| Outcome: | The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA . |
NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)
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| Challenge: | Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting. |
| Approach: | They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset. |
| Outcome: | The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset. |
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering (2021.acl-long)
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| Challenge: | Existing generative models for open-domain question answering focus on generating direct answers from unstructured textual information, but a large amount of knowledge is stored in structured databases, and need to be accessed using query languages such as SQL. |
| Approach: | They propose a hybrid framework that takes both textual and tabular evidences as input and generates either direct answers or SQL queries depending on which form could better answer the question. |
| Outcome: | The proposed framework outperforms baseline models on OpenSQuAD datasets and can generate SQL queries on the associated databases to obtain the final answers. |
Watch Every Step! LLM Agent Learning via Iterative Step-level Process Refinement (2024.emnlp-main)
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| Challenge: | Recent approaches to enhance agent performance focus on outcome rewards, which may lead to errors or suboptimal actions due to the absence of process supervision signals. |
| Approach: | They propose a step-level framework that provides detailed step-by-step guidance to enhance agent training by using Monte Carlo methods. |
| Outcome: | The proposed framework outperforms strong baselines on three tasks and shows that it is effective in augmenting efficiency and its applicability to diverse models. |
Emstremo: Adapting Emotional Support Response with Enhanced Emotion-Strategy Integrated Selection (2024.lrec-main)
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| Challenge: | Emstremo aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
| Approach: | They propose to integrate strategies and emotions into a conversational emotional support agent called Emstremo that aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
| Outcome: | Emstremo achieves strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models (2025.emnlp-main)
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Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai, Xinyue Lou, Yunghwei Lai, Ziyue Wang, Yawen Wang, Kaiyu Huang, Yile Wang, Peng Li, Yang Liu
| Challenge: | Existing multimodal benchmarks overlook linguistic and visual ambiguities, authors say . ambiguity resolution between modalities is lacking in multimodal large language models . |
| Approach: | They propose a benchmark to evaluate multimodal ambiguity resolution across multilingual and cross-modal scenarios. |
| Outcome: | a new benchmark evaluates multimodal ambiguity resolution across multilingual and cross-modal scenarios . the benchmark shows that MLLMs can resolve ambiguities in image-text alignment . however, existing benchmarks often overlook linguistic and visual ambiguties . |
InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)
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| Challenge: | Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks . |
| Approach: | They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking. |
| Outcome: | Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data. |
DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction (2024.findings-emnlp)
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| Challenge: | Chinese document-level event extraction is still largely unexplored. |
| Approach: | They propose a Chinese document-level event extraction dataset with over 36,000 events and 210,000 arguments. |
| Outcome: | The proposed dataset includes over 36,000 events and more than 210,000 arguments . it is an extension of the DocEE dataset, utilizing the same event schema and annotated by human experts. |
ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)
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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu
| Challenge: | Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem . |
| Approach: | They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths . |
| Outcome: | The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines . |
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly (2024.naacl-long)
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| Challenge: | Current large language models show imbalance abilities in different languages . authors propose two approaches to improve cross-lingual knowledge alignment . |
| Approach: | They propose a framework to assess cross-lingual knowledge alignment of large language models . they propose multilingual pretraining and multilingual instruction tuning to address this problem . |
| Outcome: | The proposed framework assesses the cross-lingual knowledge alignment of LLMs in performance, consistency and conductivity levels. |
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)
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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang
| Challenge: | Existing methods for QA in industrial environments are inherently relational and often updated. |
| Approach: | They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning. |
| Outcome: | Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity. |
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation (P19-1)
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| Challenge: | Existing methods for table-to-text generation use encoder-decoder framework, but lack of large parallel data is a problem for many domains. |
| Approach: | They propose a model to separate table-to-text generation into two stages: key fact prediction and surface realization. |
| Outcome: | The proposed model achieves 27.34 BLEU score with only 1,000 parallel data, while the baseline model only achieves 9.71 BLUE score. |
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization (2021.naacl-main)
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| Challenge: | Existing models for hierarchical text classification do not consider statistical constraint on label representations learned by structure encoder. |
| Approach: | They propose a new hierarchical text classification model called HTCInfoMax which incorporates two modules to improve the model's representations. |
| Outcome: | The proposed model can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information. |
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models (2024.findings-acl)
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Zhicheng Guo, Sijie Cheng, Hao Wang, Shihao Liang, Yujia Qin, Peng Li, Zhiyuan Liu, Maosong Sun, Yang Liu
| Challenge: | Large Language Models (LLMs) have witnessed remarkable advancements in recent years, prompting the exploration of tool learning. |
| Approach: | They propose a virtual API server and stable evaluation system to assess the stability of large-scale real-time APIs. |
| Outcome: | The proposed benchmarks demonstrate the stability of the proposed system and its caching system. |
SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at various tasks but are vulnerable to jailbreak attacks that induce harmful content generation. |
| Approach: | They propose a reinforcement learning framework that leverages the model’s own discrimination capabilities as a reward signal to enhance generation safety through iterative self-improvement. |
| Outcome: | The proposed framework improves model safety by iterative self-improvement without additional annotated data or external models during training phase. |
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)
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Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
| Challenge: | Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks . |
| Approach: | They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps. |
| Outcome: | The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Contextualized Semantic Distance between Highly Overlapped Texts (2023.findings-acl)
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| Challenge: | Conventional semantic metrics are based on word representations and are vulnerable to disturbance of overlapped components with similar representations. |
| Approach: | They propose a mask-and-predict strategy to evaluate the semantic distance between the overlapped sentences using words in the longest common sequence as neighboring words and use masked language modeling to predict their positions. |
| Outcome: | The proposed method outperforms the state-of-the-art in domain adaption by a huge margin. |
LLM-Guided Semantic-Aware Clustering for Topic Modeling (2025.acl-long)
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| Challenge: | Experimental results show that topic modeling is competitive compared to closed-source methods. |
| Approach: | They propose a semi-supervised topic modeling method that combines LLMs with clustering to improve topic generation and distribution. |
| Outcome: | The proposed method outperforms state-of-the-art methods that utilize GPT-4 on topic alignment and exhibits competitive performance compared to Neural Topic Models on topic quality. |
LaMPE: Length-aware Multi-grained Positional Encoding for Adaptive Long-context Scaling Without Training (2026.findings-acl)
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| Challenge: | Large language models (LLMs) experience significant performance degradation when the input exceeds the pretraining context window due to the out-of-distribution (OOD) behavior of Rotary Position Embedding (RoPE). |
| Approach: | They propose a training-free method that remaps out-of-distribution (OOD) positions into the in-distance range with fixed mapping strategies, ignoring the dynamic relationship between input length and effective context window. |
| Outcome: | Experiments on three representative LLMs across five mainstream long-context benchmarks show that the proposed method achieves significant performance improvements compared to existing methods. |
Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)
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Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang
| Challenge: | Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages. |
| Approach: | They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems. |
| Outcome: | The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function. |
MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)
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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao
| Challenge: | Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence. |
| Approach: | They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory. |
| Outcome: | The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%. |
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)
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| Challenge: | Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance . |
| Approach: | They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences . |
| Outcome: | The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs. |
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)
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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
| Challenge: | Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced. |
| Approach: | They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model. |
| Outcome: | The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs. |
Towards Robust Speech Representation Learning for Thousands of Languages (2024.emnlp-main)
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William Chen, Wangyou Zhang, Yifan Peng, Xinjian Li, Jinchuan Tian, Jiatong Shi, Xuankai Chang, Soumi Maiti, Karen Livescu, Shinji Watanabe
| Challenge: | XEUS is a cross-lingual encoder for universal speech that can be trained on 1 million hours of data across 4057 languages. |
| Approach: | They propose a Cross-lingual Encoder for Universal Speech that can be trained on 1 million hours of data across 4057 languages and a newly created corpus of 7400+ hours from 4057 . |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks and outperfies MMS 1B and w2v-BERT 2.0 v2 by 0.8% and 4.4% respectively. |
On the Faithfulness for E-commerce Product Summarization (2020.coling-main)
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| Challenge: | e-commerce product summarization requires consistency between product attributes and summary . inconsistent product summaries can mislead users and decrease public credibility . |
| Approach: | They propose a model to generate e-commerce product summaries with product attributes . they encode product attribute table and constrain attribute words to be presented only through copying . |
| Outcome: | The proposed model significantly improves the faithfulness of e-commerce product summarization tasks. |
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference (2024.findings-emnlp)
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| Challenge: | Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources for inference . the growth of their multimodal Key-Value (KV) cache challenges memory and time efficiency. |
| Approach: | They propose a fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache. |
| Outcome: | The proposed method reduces the multimodal KV cache size while maintaining performance comparable to a full cache. |
When Safety Alignment Fails to Generalize: Probing with Language Game Jailbreaks (2026.findings-acl)
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| Challenge: | Existing safety alignment methods rely on fixed or narrow transformation schemes to generalize . existing methods based on fixed and narrow transformations are often inadequate . |
| Approach: | They propose a framework for discovering and refining language game-based jailbreaks to probe alignment generalization. |
| Outcome: | The proposed framework allows controlled exploration of alignment behavior across closely related linguistic variants. |
TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models (2026.findings-acl)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to autoregressive reasoning . SpecReason adopts a polling-based design that repeatedly invokes the LRM for verification at every step . |
| Approach: | They propose a trigger-based collaborative reasoning framework that delegates most reasoning to the SRM and activates LRM intervention only when necessary. |
| Outcome: | The proposed framework reduces latency and API cost by 73.3% under edge–cloud conditions. |
Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)
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| Challenge: | Named entity recognition (NER) is a key task reliant on textual data. |
| Approach: | They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries. |
| Outcome: | The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets. |
Revisiting Source Context in Nearest Neighbor Machine Translation (2023.emnlp-main)
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| Challenge: | Existing research does not explicitly consider the source context when retrieving similar examples . |
| Approach: | They propose a method to improve neural machine translation via source context enhancement by integrating a source-aware distance calibration module. |
| Outcome: | The proposed approach can be integrated with representative kNN-MT baselines and achieve significant performance improvements. |
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction (2021.findings-acl)
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Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Distantly supervised relation extraction (RE) has attracted much attention in the past few years . previous methods to evaluate models manually or directly on autolabeled data have produced inaccurate evaluations . |
| Approach: | They propose to use distant supervision to generate large-scale autolabeled data . they build manually-annotated test sets for two DS-RE datasets and evaluate models . |
| Outcome: | The proposed method produces 53% wrong labels at the entity pair level in the popular NYT10 dataset. |
Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from multi-modality problem . despite their autoregressivity, most NMT models suffer with slow decoding speed . |
| Approach: | They propose a semi-autoregressive model which generates a translation as a sequence of segments while each segment is predicted token-by-token. |
| Outcome: | The proposed model can achieve 4 times speedup while maintaining comparable performance. |
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition (2021.naacl-main)
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| Challenge: | Existing models fail to fully utilize contextual information which plays an important role in interpreting sentences. |
| Approach: | They propose a graph-based Context Tracking Network to model the discourse context for IDRR. |
| Outcome: | The proposed model can integrate sentence-level and token-level contextual semantics better than existing models. |
TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use (2025.findings-emnlp)
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Junjie Ye, Yilong Wu, Sixian Li, Yuming Yang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan, Zhengyin Du
| Challenge: | a new approach to training large language models (LLMs) overlooks task-specific characteristics in tool use, leading to performance bottlenecks. |
| Approach: | They propose a task-feature-based framework that mitigates the effects of suboptimal training data . they use a dataset to train large-scale LLMs and a reward mechanism tailored to error categories . |
| Outcome: | The proposed framework matches or surpasses open- and closed-source LLMs in tool-use performance using only 1,217 training data points. |
Pre-training Distillation for Large Language Models: A Design Space Exploration (2025.acl-long)
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| Challenge: | Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model for model compression. |
| Approach: | They extend knowledge distillation to the pre-training phase of large language models . they first conduct an experiment using a teacher LLM to distill a 1.9B student LLM . |
| Outcome: | The proposed model can be used to distill a 1.9B student model using a teacher LLM. |
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)
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Jiaming Wang, Yunke Zhao, Peng Ding, Jun Kuang, Yibin Shen, Zhe Tang, Yilin Jin, ZongYu Wang, Xiaoyu Li, Xuezhi Cao
| Challenge: | Existing models lack the ability to adhere to instructions, resulting in suboptimal performance. |
| Approach: | They propose an automated iterative instruction-following benchmark with integrated feedback mechanism. |
| Outcome: | The proposed benchmark identifies erroneous components in model responses and provides feedback accurately. |
Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance, but they keep repeating similar mistakes due to their inability to capture relationships among samples. |
| Approach: | They propose a tuning-free rule accumulation framework that guides LLMs in improving their performance by learning from previous mistakes. |
| Outcome: | The proposed framework improves over baselines by a large margin over previous frameworks. |
Rethinking Stealthiness of Backdoor Attack against NLP Models (2021.acl-long)
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| Challenge: | Existing backdoor attacks are not stealthy to system deployers or users. |
| Approach: | They propose a novel backdoor attack method based on negative data augmentation and modifying word embeddings that is much stealthier while maintaining pretty good attacking performance. |
| Outcome: | The proposed method is much stealthier while maintaining pretty good attacking performance. |
VoiceStar: Robust Zero-Shot Autoregressive TTS with Duration Control and Extrapolation (2026.findings-acl)
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| Challenge: | Neural codec language models (NCLMs) lack fine-grained controllability and inability to extrapolate to sequence lengths much longer than those seen during training. |
| Approach: | They propose a novel autoregressive encoder-decoder neural codec language model that can be trained with a Continuation-Prompt Mixed training system. |
| Outcome: | The proposed model outperforms or is on par with current state-of-the-art models on short-form benchmarks such as LibriSpeech and Seed-TTS in terms of intelligibility and naturalness. |
Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for fact-checking text generated by large language models are expensive and time-consuming. |
| Approach: | They propose a plug-and-play framework that harnesses large language models for efficient fact-checking in a few-shot manner. |
| Outcome: | The proposed framework is compared with state-of-the-art models and shows that it can be used to speed up fact-checking in a few-shot manner. |
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)
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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott
| Challenge: | FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China. |
| Approach: | They evaluate vision–language Models and large language models on unseen food images and corresponding questions. |
| Outcome: | The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions. |
DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data. |
| Approach: | They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain. |
| Outcome: | The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings. |
Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach (2024.findings-acl)
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| Challenge: | Large Language Models generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt. |
| Approach: | They propose to refine a Large Language Model (LLM) with prompt-output pairs with equivalent semantics to achieve semantic consistency. |
| Outcome: | The proposed method improves the semantic consistency and task performance of LLMs. |
Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.acl-long)
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Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang
| Challenge: | a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations . |
| Approach: | They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems. |
| Outcome: | The proposed toolkit reveals critical robustness issues in state-of-the-art models. |
Contextual Modeling for Document-level ASR Error Correction (2024.lrec-main)
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| Challenge: | Existing work on document-level ASR error correction ignores contextual information . however, there are limited studies on incorporating contextual information into AEC . |
| Approach: | They propose a context-aware method that retrieves contextual information from a datastore . they use two English and two Chinese datasets to model document-level AEC . |
| Outcome: | The proposed model can utilize contextual information to improve document-level AEC . the data store containing contextual information provides even better results . |
FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (2023.acl-long)
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| Challenge: | Existing Universal Information Extraction models rely heavily on span boundaries in data during training, which does not reflect the reality of span annotation challenges. |
| Approach: | They propose a framework that uses fuzzy spans to model various IE tasks . they propose generative Universal Information Extraction (UIE) to unify various ie tasks based on fuzzy span boundaries . |
| Outcome: | The proposed framework improves on a series of main IE tasks with small amounts of data and training epochs. |
Fully Hyperbolic Neural Networks (2022.acl-long)
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| Challenge: | Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space. |
| Approach: | They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks. |
| Outcome: | The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models . |
SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are widely deployed as domain-specific agents, but evaluation of their capabilities in such contexts has not been fully explored. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to follow instructions and make decisions in real-world scenarios. |
| Outcome: | The proposed benchmark is constructed from real-world business data and adapted into 23 complex SOP scenarios. |
A Challenge Dataset and Effective Models for Conversational Stance Detection (2024.lrec-main)
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| Challenge: | stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis. |
| Approach: | They propose a multi-turn conversation stance detection dataset that encompasses multiple targets for conversational stance detector. |
| Outcome: | The proposed dataset encompasses multiple targets for conversational stance detection. |
Paraphrase Generation as Unsupervised Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for paraphrase generation rely on labeled datasets or are limited in narrow domains. |
| Approach: | They propose a paradigm for paraphrase generation by treating the task as unsupervised machine translation based on pairs of unlabeled monolingual sentences. |
| Outcome: | The proposed paradigm can generate paraphrases on a large unlabeled monolingual corpus without relying on bilingual sentence pairs. |
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension (2023.findings-acl)
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| Challenge: | Compared to standard RC tasks, dialogue reading comprehension (DRC) has raised challenges because of the complex speaker information and noisy dialogue context. |
| Approach: | They propose a new method for dialogue reading comprehension that extracts answers from dialogues by using key-utterances-extracting methods and a Question-Interlocutor Scope Realized Graph. |
| Outcome: | The proposed method achieves state-of-the-art performance against previous works. |
MIND: Towards Immersive Psychological Healing with Multi-Agent Inner Dialogue (2025.findings-emnlp)
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Yujia Chen, Changsong Li, Yiming Wang, Tianjie Ju, Qingqing Xiao, Nan Zhang, Zifan Kong, Peng Wang, Binyu Yan
| Challenge: | Mental health issues are worsening in today’s competitive society, such as depression and anxiety. |
| Approach: | They propose a multi-agent inner dialogue paradigm that provides more immersive psychological healing environments. |
| Outcome: | The proposed paradigm provides more immersive psychological healing environments. |
Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)
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Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo
| Challenge: | Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. |
| Approach: | They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance. |
| Outcome: | The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50. |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (D19-1)
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| Challenge: | Existing models for reading comprehension and question answering do not support discrete reasoning abilities. |
| Approach: | They propose a reading comprehension model that uses a multi-type answer predictor and a multiple-span extraction method to produce one or multiple text spans. |
| Outcome: | The proposed model achieves 79.9 F1 on the DROP hidden test set, creating new state-of-the-art results. |
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages (2024.acl-long)
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| Challenge: | Contemporary large language models (LLMs) are pre-trained on multilingual corpora, but their performance lags behind in most languages compared to a few resource-rich languages. |
| Approach: | They propose a method that leverages the internal capabilities of large language models on resource-rich languages to enhance multilingual performance. |
| Outcome: | The proposed method improves multilingual performance while minimizing impact on original performance in resource-rich languages. |
Rethinking Positional Encoding in Tree Transformer for Code Representation (2022.emnlp-main)
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| Challenge: | Recent works have proposed novel tree Transformers to capture the syntactic structure in source code. |
| Approach: | They propose a novel tree Transformer encoding node positions based on a description method for tree structures to incorporate inductive bias into Transformer. |
| Outcome: | The proposed model outperforms baselines on code summarization and completion tasks across two languages, and it is able to perform better on both local and global paradigms. |
On the Role of Discriminative Models in Generative Relation Extraction (2026.acl-long)
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| Challenge: | Existing methods for relation extraction (RE) are discriminative and generative . previous studies show that discriminative models can support generative RE . |
| Approach: | They propose a framework that leverages discriminative models to produce a top-k set of candidate relations and integrates this knowledge into generative models via in-context or prompt learning. |
| Outcome: | The proposed framework achieves state-of-the-art on five widely used RE benchmarks. |
HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)
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| Challenge: | Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles. |
| Approach: | They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles. |
| Outcome: | The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy. |
From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models (2026.findings-acl)
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| Challenge: | Long-context capabilities are essential for document and video understanding, in-contact learning, and inference-time scaling. |
| Approach: | They propose an efficient training recipe for building ultra-long context LLMs from aligned instruct model, pushing the boundaries of context lengths from 128K to 1M, 2M, and 4M tokens. |
| Outcome: | The proposed model extends the context window while maintaining short context capabilities while maintaining the performance of the existing model. |
GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding (2025.acl-long)
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| Challenge: | Programming languages have rich semantics that are represented by graphs and not available from the surface form of source code. |
| Approach: | They propose to use graph neural networks and cross-modal alignment technologies to inject structural information of code into LLMs as an auxiliary task during finetuning. |
| Outcome: | The proposed framework improves on five code tasks with six different baseline LLMs, while incurring no cost at inference time. |
Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation (2021.findings-acl)
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| Challenge: | Existing studies focus on implicit exploration of multimodal coreference but neglect the importance of locating the objects explicitly in the visual content, which is associated with textual entities. |
| Approach: | They propose a multimodal incremental transformer with visual grounding which aims to explicitly locate related objects in the image guided by textual entities. |
| Outcome: | The proposed model achieves comparable performance on the VisDial v0.9 and v1.0 datasets. |
CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory (2025.acl-long)
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Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Existing ground VLN agents struggle in aerial VLLN due to the lack of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration. |
| Approach: | They propose a large language model-empowered aerial VLN agent that decomposes the long-horizon task into sub-goals with different semantic levels. |
| Outcome: | The proposed method achieves state-of-the-art performance with significant improvement in continuous city environments. |
Learn and Consolidate: Continual Adaptation for Zero-Shot and Multilingual Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Existing multilingual neural machine translation models perform poorly on language pairs with no parallel corpus. |
| Approach: | They propose a two-stage approach that encourages original models to acquire language-agnostic multilingual representations from new data and preserves the model architecture without introducing parameters. |
| Outcome: | The proposed approach improves performance in translation directions where existing models are weak and mitigates degeneration in the well-performing translation directions, offering flexibility in the real-world scenario. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Recent studies have highlighted the lack of adversarial robustness in pre-trained models. |
| Approach: | They propose a fine-tuning approach that conducts selective updates when adapting pre-trained models to downstream tasks. |
| Outcome: | The proposed approach improves adversarial robustness on downstream tasks . it eliminates spurious updates, leading to flatter and wider optima than the conventional method . |
Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026.acl-industry)
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Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang
| Challenge: | Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet traditional singleround retrieval struggles with complex multistep reasoning. |
| Approach: | They propose a framework that introduces path-centric reward shaping for agentic RAG training. |
| Outcome: | The proposed framework improves on existing methods with an average accuracy gain of 7.7 points. |
Exploring Reasoning Reward Model for Agents (2026.findings-acl)
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Kaixuan Fan, Kaituo Feng, Manyuan Zhang, Tianshuo Peng, Zhixun Li, Yilei Jiang, Shuang Chen, Xiangyu Yue
| Challenge: | Existing methods for agentic reinforcement learning rely on sparse outcome-based reward for training, leading to suboptimal results. |
| Approach: | They propose an agent-based reward model that produces structured feedback for agentic trajectories, including an explicit reasoning trace and a focused critique. |
| Outcome: | The proposed model produces structured feedback for agentic trajectories including an explicit reasoning trace, a focused critique, and an overall score that evaluates process performance. |
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)
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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
| Challenge: | a recent study shows that retrieval-augmented LMs can improve text generation quality and accuracy. |
| Approach: | They propose a model that reproduces RETRO parameters while retrieving a text corpus . they find RETRO outperforms GPT on text generation with less repetition . |
| Outcome: | The proposed model outperforms standard retrieval-augmented GPT and retrieval augmented GTP on text generation and accuracy tasks. |
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately . |
| Approach: | They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes . |
| Outcome: | The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show . |
SPEAK: Spiking Neurons as an Entropy-Aware Tokenizer for Large Language Models (2026.acl-long)
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| Challenge: | Existing tokenizers fail to explicitly leverage historical tokenization results . large language models (LLMs) have demonstrated remarkable effectiveness across NLP tasks . |
| Approach: | They propose a tokenizer that integrates spiking neurons to explicitly leverage historical tokenization results. |
| Outcome: | The proposed tokenizer leverages historical tokenization results, but does not selectively leverage history based on contextual relevance. |
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models (2022.findings-emnlp)
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| Challenge: | Existing models for natural language processing (NLP) are fine-tuned and released for research and deployments. |
| Approach: | They propose a PLM reuse paradigm that merges teacher-PLM knowledge into a student model. |
| Outcome: | The proposed paradigm can reduce the computational cost and environmental side-effects of retraining the PLM from scratch. |
A Class-Rebalancing Self-Training Framework for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Distant supervision reduces the reliance on human annotation in named entity recognition tasks. |
| Approach: | They propose a class-rebalancing self-training framework for improving distantly-supervised named entity recognition by using a flexible threshold and a hybrid pseudo label. |
| Outcome: | The proposed model achieves state-of-the-art on five flat and two nested datasets and compares with other methods on the same dataset. |
SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting (2023.findings-emnlp)
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| Challenge: | Experimental results show that SGP-TOD provides state-of-the-art zero-shot performance . prevailing approach for creating task bots is to fine-tune pre-trained language models . |
| Approach: | They propose a Schema-Guided Prompting for building Task-Oriented Dialog systems . they use predefined task schema and dialog policy to instruct fixed LLMs to generate appropriate responses . |
| Outcome: | The proposed system outperforms few-shot approaches on multiwoz, RADDLE, and STAR datasets. |
Event Detection with Dual Relational Graph Attention Networks (2022.coling-1)
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| Challenge: | Existing approaches to event detection confuse syntactic relations and introduce redundant or noisy information, leading to performance degradation. |
| Approach: | They propose a model that exploits syntactic and semantic relations to alleviate the problem by combining syntatic and semantic knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a ACE2005 benchmark dataset. |
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)
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Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou
| Challenge: | Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering. |
| Approach: | They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text. |
| Outcome: | The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings. |
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge in natural language tasks. |
| Approach: | They propose a framework that enables LLMs to ask relevant questions to uncover more details in the image, along with filters for refining the generated information. |
| Outcome: | The proposed framework boosts the performance of baseline methods by 2.15% on OK-VQA and achieves consistent improvements across different LLMs. |
CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)
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| Challenge: | Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents. |
| Approach: | They present a problem of cross-document relation extraction (CRE) using human annotations. |
| Outcome: | The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models. |
StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs (2026.findings-acl)
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| Challenge: | Prior work focused on typographic and pixel-level perturbations, leaving the study of SCO unexplored. |
| Approach: | They propose a framework that exploits MLLMs' diagrammatic reasoning capabilities to bypass safety guardrails. |
| Outcome: | The proposed framework exploits the model's reasoning capabilities to bypass safety guardrails. |
On the Continued Value of Universal Dependencies in the Era of Large Language Models (2026.acl-long)
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| Challenge: | a growing belief that explicit linguistic representations are no longer necessary is questioned in large language models . a recent study examines whether and in what ways this cross-lingual syntactic framework can still benefit LLMs . |
| Approach: | They use Universal Dependencies (UD) to examine whether and in what ways it can still benefit LLMs. |
| Outcome: | The proposed model outperforms its syntax-agnostic counterparts in a cross-lingual evaluation task. |
METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling (2025.acl-long)
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| Challenge: | Chart generation requires strong visual design skills and precise coding capabilities that embed the desired visual properties into code. |
| Approach: | They propose a vision-language model-based multi-agent framework for effective automatic chart generation. |
| Outcome: | The proposed framework achieves a 5.2% improvement in the F1 score over the current best chart generation task. |
T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)
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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li
| Challenge: | Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications. |
| Approach: | They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning. |
| Outcome: | The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation. |
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)
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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo
| Challenge: | a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Approach: | They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Outcome: | The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains. |
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)
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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang
| Challenge: | ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets. |
| Approach: | They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation. |
| Outcome: | The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models. |
Self-Knowledge Guided Retrieval Augmentation for Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown superior performance without task-specific fine-tuning due to the computational costs. |
| Approach: | They propose a method which lets LLMs refer to the questions they have previously encountered and adaptively call for external resources when dealing with new questions. |
| Outcome: | The proposed method outperforms chain-of-thought based and fully retrieval-based methods on multiple datasets and outperformed chain- of-though, chatGPT and InstructGPT. |
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention (D18-1)
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| Challenge: | Existing methods for relation extraction use knowledge graphs to automatically label training data . but, it suffers from the wrong labeling problem because not all sentences containing two entities can express their relations in KGs . |
| Approach: | They propose a distant supervision approach to automatically label training instances . they integrate hierarchical information of relations into distantly supervised relation extraction . |
| Outcome: | The proposed model outperforms baseline models on a large-scale dataset. |
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection (2022.coling-1)
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| Challenge: | stance detection is a task to identify attitudes from opinions towards certain targets, but it is expensive and time-consuming . stance detector is based on labeled data, but unlabeled data can be collected easier . |
| Approach: | They propose a semi-supervised framework for few-shot stance detection that uses unlabeled data to learn more distinguishable representations for different targets. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple benchmark datasets. |
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying. |
| Approach: | They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one. |
| Outcome: | The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying . |
DRS: Deep Question Reformulation With Structured Output (2025.findings-acl)
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| Challenge: | Existing models like GPT-3 and Instruct-GPT lack the ability to reformulate unanswerable questions. |
| Approach: | They propose a zero-shot method that combines the strengths of LLMs with a DFS-based algorithm to iteratively explore potential entity combinations and constrain outputs using predefined entities. |
| Outcome: | The proposed method outperforms all baselines, including the GPT-3.5 model, on the unanswerable question reformulation task. |
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction (2024.lrec-main)
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| Challenge: | Existing methods for relational triple extraction (RTE) are unnatural and recast RTE tasks to text-to-text prompting formats. |
| Approach: | They propose a tabular prompting for RTE which frames RTE task into a table generation task and propose an instructive in-context learning which only selects and annotates samples considering triple semantics in massive unlabeled samples. |
| Outcome: | The proposed prompting for RTE with TableIE achieves state-of-the-art performance compared to other methods . the proposed prompts are based on off-the shelf LLMs and are scalable to multiple scenarios . |
Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching (2021.tacl-1)
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| Challenge: | Existing methods for building task-oriented dialog systems are limited to a few tasks and domains. |
| Approach: | They propose a method that uses transfer learning and machine teaching to build task bots at scale. |
| Outcome: | The proposed method outperforms existing methods on well-studied task-oriented dialog benchmarks on well studied tasks. |
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)
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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li
| Challenge: | Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) . |
| Approach: | They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries. |
| Outcome: | The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. |
Guided Dialogue Policy Learning without Adversarial Learning in the Loop (2020.findings-emnlp)
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Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao
| Challenge: | Reinforcement learning methods suffer from sparse and unstable reward signals . alternating training of dialogue agent and reward model can get stuck in local optima . |
| Approach: | They propose to decompose adversarial training into two steps to improve dialogue policy learning. |
| Outcome: | The proposed method achieves remarkable task success rate using both on-policy and off-poly reinforcement learning methods. |
MAVEN-FACT: A Large-scale Event Factuality Detection Dataset (2024.findings-emnlp)
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| Challenge: | Event factuality detection is under-explored due to the lack of high-quality large-scale data . efd is a subfield of event understanding, which aims to determine the factuity of textual events. |
| Approach: | They propose a large-scale EFD dataset with factuality annotations of 112,276 events . they find that adopting event arguments and relations helps in event factuity detection . |
| Outcome: | The proposed dataset includes factuality annotations of 112,276 events . it is the largest EFD dataset and is challenging for fine-tuned models and large language models . |
LoRAExit: Empowering Dynamic Modulation of LLMs in Resource-limited Settings using Low-rank Adapters (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have exhibited remarkable performance across various natural language processing tasks, but deployment on resource-limited settings remains a challenge. |
| Approach: | They propose a dynamic inference architecture that leverages low-rank adaptors for efficient deployment of LLMs. |
| Outcome: | The proposed architecture significantly improves performance when deployed on resource-limited settings. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design (2025.naacl-long)
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| Challenge: | Using Mixture-of-Experts, researchers have found that efficient MoE is difficult to achieve due to two key reasons: imbalanced expert activation and massive communication overhead. |
| Approach: | They propose a collaboration-constrained routing strategy that encourages more specialized expert groups and leverages expert specialization. |
| Outcome: | The proposed approach achieves an average performance improvement of 0.51% and 0.33% on LLaMA-MoE and Qwen-MaE respectively. |
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)
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| Challenge: | Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations. |
| Approach: | They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. |
| Outcome: | The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations. |
Entropy-Based Vocabulary Substitution for Incremental Learning in Multilingual Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to update a multilingual model with new language pairs are expensive and time-consuming. |
| Approach: | They propose an entropy-based vocabulary substitution method that walks through new language pairs for incremental learning while remaining the size of the original vocabulary. |
| Outcome: | The proposed method achieves better performance and saves excess overhead in a multilingual machine translation task. |
Automatic Marketing Theme and Commodity Construction System for E-commerce (2023.emnlp-industry)
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| Challenge: | Existing recommendation system invites experts to write marketing themes and select relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. |
| Approach: | They propose to use pretrained language model to generate marketing themes and commodity consistency module to select relevant commodities for the generative theme. |
| Outcome: | The proposed system can generate popular marketing themes and select relevant commodities automatically and improve theme online effectiveness compared with state-of-the-art methods. |
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels (2025.emnlp-main)
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Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan
| Challenge: | Large language models (LLMs) acquire substantial world knowledge during pretraining, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). |
| Approach: | They evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLama-3 families and examine the impact of supervised fine-tuning on model knowledge. |
| Outcome: | The proposed model performance is 14% worse than models fine-tuned on 1,920 samples and 12% worse on 240 samples. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
Weakly Supervised Vision-and-Language Pre-training with Relative Representations (2023.acl-long)
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| Challenge: | Weakly supervised vision-and-language pre-training (WVLP) uses only local descriptions of images as cross-modal anchors to construct weakly-aligned image-text pairs for pre- training. |
| Approach: | They propose to take a small number of aligned image-text pairs as anchors and represent each unaligned image and text by its similarities to these anchors. |
| Outcome: | The proposed model reduces the cost of pre-training while maintaining decent performance on downstream tasks. |
ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval (2024.lrec-main)
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| Challenge: | Recent studies have proposed tool learning, which augments LLMs with external tools. |
| Approach: | They propose an adaptive and hierarchy-aware reranking method to refine retrieval results by truncating the retrieval result related to seen and unseen tools at different positions. |
| Outcome: | The proposed method improves retrieval results, leading to better execution results generated by the LLM. |
HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization (2024.lrec-main)
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| Challenge: | Existing benchmarks focus on translating English prompts to multilingual codes or have been constrained to very limited natural languages (NLs). |
| Approach: | They propose a benchmark to evaluate multilingual LLMs using multiple natural languages. |
| Outcome: | The proposed benchmarks focus on translating English prompts to multilingual code or have been constrained to very limited natural languages (NLs). |
Do we need Label Regularization to Fine-tune Pre-trained Language Models? (2023.eacl-main)
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Ivan Kobyzev, Aref Jafari, Mehdi Rezagholizadeh, Tianda Li, Alan Do-Omri, Peng Lu, Pascal Poupart, Ali Ghodsi
| Challenge: | Knowledge Distillation (KD) is a label regularization technique that can be replaced with lighter teacher-free variants such as the label-smoothing technique. |
| Approach: | They propose to use knowledge distillation to train student models by deploying the teacher network during training. |
| Outcome: | The proposed method can be replaced with lighter teacher-free variants on PLMs with more than 600 distinct trials and ran each configuration five times. |
T-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional Chinese Medicine (C18-2)
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| Challenge: | Traditional Chinese Medicine (TCM) is one of precious intangible cultural heritages of the Chinese nation. |
| Approach: | They propose to use authorized and anonymized clinical records, medicine clinical guidelines, teaching materials, classic medical books, academic publications, etc. as data resources to build a TCM knowledge graph. |
| Outcome: | The proposed system extracts triples from free texts to build a TCM knowledge graph. |
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)
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| Challenge: | Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process . |
| Approach: | They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders . |
| Outcome: | The proposed approach can be used to make an effective decision-making tool for the academic paper review process. |
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog (2021.findings-acl)
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| Challenge: | Experimental results show that our model outperforms the strong baseline in both generative and discriminative settings by a significant margin. |
| Approach: | They propose a relation-aware graph-over-graph network (GoG) for visual dialog . their model outperforms the strong baseline in both generative and discriminative settings . |
| Outcome: | The proposed model outperforms baseline models in both generative and discriminative settings by a significant margin. |
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack prior and posterior knowledge selection . prior selection module may not learn to select knowledge properly because of lack of posterior information . |
| Approach: | They propose a knowledge distillation-based training strategy to remove the exposure bias of knowledge selection. |
| Outcome: | The proposed model improves on two knowledge-grounded dialogue datasets. |
Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems (2025.acl-long)
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| Challenge: | Existing reward models focus on human preferences, neglecting verifiable correctness signals. |
| Approach: | They propose a reward system that combines human preference rewards with verifiable correctness signals to provide reliable rewards. |
| Outcome: | The proposed reward agent significantly outperforms vanilla reward models on benchmarks and inference-time best-of-n searches on real-world tasks. |
Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)
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| Challenge: | Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance. |
| Approach: | They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text. |
| Outcome: | The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods. |
RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)
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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries. |
| Approach: | They propose a framework for training query rewriting models that leverages a reranker framework. |
| Outcome: | The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models. |
ELLE: Efficient Lifelong Pre-training for Emerging Data (2022.findings-acl)
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| Challenge: | Existing pre-trained language models are typically trained with static data, ignoring that streaming data of various sources may continuously grow. |
| Approach: | They propose to use function preserved model expansion to expand existing PLM's width and depth to improve efficiency of knowledge acquisition. |
| Outcome: | The proposed model improves pre-training efficiency and performance over existing models on BERT and GPT. |
DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)
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Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang, Fuwen Luo, Weizheng Qiao, Lu Han, Ziwei Sun, Peng Li, Yang Liu
| Challenge: | Dongba pictographic is the only pictograph script still in use in the world. |
| Approach: | DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs. |
| Outcome: | The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs. |
How Does the Disclosure of AI Assistance Affect the Perceptions of Writing? (2024.emnlp-main)
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| Challenge: | Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows. |
| Approach: | They conduct an experimental study to determine whether disclosure of AI assistance in the writing process would affect people's evaluation on the quality of the writing and ranking of different writings. |
| Outcome: | The disclosure of AI assistance decreases the average quality ratings for argumentative essays and creative stories, and increases the quality of the writings. |
RedundancyLens: Revealing and Exploiting Visual Token Processing Redundancy for Efficient Decoder-Only MLLMs (2025.findings-acl)
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| Challenge: | Current decoder-only architectures achieve higher performance but lower efficiency . cross-attention-based architectures skip visual token computations . |
| Approach: | They propose a training-free framework for analyzing trained MLLMs to investigate redundancy . they propose 'probe-activated Dynamic FFN and Hollow Attention' algorithms for visual token reductions and a layer ranking algorithm for inference acceleration. |
| Outcome: | The proposed framework achieves comparable performance to or better than state-of-the-art methods while remaining compatible with them. |
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)
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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang
| Challenge: | Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored. |
| Approach: | They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes. |
| Outcome: | The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |
Beyond the Panorama: Training-Free Hierarchical Perception-Reasoning for Fine-Grained Vision in MLLMs (2026.acl-long)
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| Challenge: | Existing multimodal large language models (MLLMs) face challenges in fine-grained visual tasks. |
| Approach: | They propose a training-free hierarchical perception-reasoning framework that enhances fine-grained visual understanding by simulating human perception mechanisms. |
| Outcome: | The proposed framework enhances fine-grained visual understanding by simulating human perception mechanisms. |
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for fine-tuning pretrained language models suffer from poor generalization . however, they add a perturbation to each model parameter equally, which is sub-optimal . |
| Approach: | They propose a sharpness-aware minimization optimization procedure that introduces a Fisher mask to improve the efficiency of SAM. |
| Outcome: | The proposed method outperforms the vanilla sharpness-aware minimization method on GLUE and SuperGLUE benchmarks. |
Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)
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| Challenge: | Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation. |
| Approach: | They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt. |
| Outcome: | The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks. |
A Template-based Method for Constrained Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to solve this problem can not satisfy the following three desiderata: (1) high translation quality, (2) high match accuracy, and (3) low latency. |
| Approach: | They propose a template-based method that can provide high translation quality and match accuracy and a low latency inference. |
| Outcome: | The proposed method outperforms baselines in lexically and structurally constrained translation tasks and can be used in a variety of applications. |
Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)
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| Challenge: | Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks. |
| Approach: | They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities. |
| Outcome: | The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios. |
Natural Language Processing Meets Quantum Physics: A Survey and Categorization (2021.emnlp-main)
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| Challenge: | Recent research has focused on quantum-inspired algorithms for NLP and quantum-based algorithms for cognition. |
| Approach: | They propose to categorize quantum-inspired algorithms according to quantum theory, linguistic targets that are modeled, and the downstream application. |
| Outcome: | The proposed methods are categorized according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application. |
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (P19-1)
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| Challenge: | Existing approaches to answer reading comprehension tasks are inefficient since the input is re-encoded within each module. |
| Approach: | They propose a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD. |
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data. |
| Approach: | They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy. |
| Outcome: | The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks. |
MMD-ERE: Multi-Agent Multi-Sided Debate for Event Relation Extraction (2025.coling-main)
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| Challenge: | Existing research indicates that LLMs can be overconfident and stubborn. |
| Approach: | They propose a multi-agent multi-sided debate approach for event relation extraction which explores the understanding of event relations between different participants before and after the debate. |
| Outcome: | The proposed approach outperforms established baselines on various ERE tasks and LLMs. |
Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning. |
| Approach: | They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs. |
| Outcome: | The proposed method outperforms baselines on multiple languages while maintaining performance on original language pairs. |
ConvLab: Multi-Domain End-to-End Dialog System Platform (P19-3)
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Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao
| Challenge: | ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
| Approach: | They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments. |
| Outcome: | The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Activation-Guided Local Editing for Jailbreaking Attacks (2026.acl-long)
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| Challenge: | Existing methods for jailbreaking Large Language Models (LLMs) are limited and produce incoherent or unreadable inputs. |
| Approach: | They propose a two-stage framework that performs a one-shot, scenario-based generation of context and rephrases the original malicious query to obscure its harmful intent. |
| Outcome: | The proposed framework achieves state-of-the-art Attack Success Rate, with gains of up to 37.74% over the strongest baseline, and excellent transferability to black-box and large-scale models. |
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)
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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li
| Challenge: | Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. |
| Approach: | They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone . |
| Outcome: | Experiments on four TVR datasets show that the proposed method performs better than other methods. |
Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning? (2025.acl-long)
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Yancheng He, Shilong Li, Jiaheng Liu, Weixun Wang, Xingyuan Bu, Ge Zhang, Z.y. Peng, Zhaoxiang Zhang, Zhicheng Zheng, Wenbo Su, Bo Zheng
| Challenge: | Recent advances in o1-like models have generated long Chain-of-Thought reasoning steps to improve the reasoning abilities of existing Large Language Models (LLMs). |
| Approach: | They propose a DeltaBench to analyze the quality and effectiveness of o1-like models and measure their ability to detect errors in long COT reasoning. |
| Outcome: | The proposed model can detect errors in long COT reasoning. |
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models (2023.acl-long)
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| Challenge: | Existing knowledge distillation methods require access to internal information of teachers . however, such information is not always accessible for large pre-trained language models . |
| Approach: | They propose a method to estimate logits from the decision distributions using logits theoretically and empirically. |
| Outcome: | The proposed method outperforms baselines on natural language understanding and machine reading comprehension datasets. |
EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition (2025.findings-emnlp)
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| Challenge: | Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities, but their performance in affective computing tasks remains suboptimal. |
| Approach: | They propose a framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). |
| Outcome: | The proposed framework improves LALMs' reasoning abilities on MELD and IEMOCAP datasets and shows strong generalization. |
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs (2024.findings-acl)
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| Challenge: | Large language models have demonstrated considerable capabilities across various tasks . however, they often fall short of the performance achieved by domain-specific state-of-the-art models . |
| Approach: | They propose a tuning-free method to augment domain-specific abilities of Large language models . they leverage insights from the response preference of expert models to augment LLMs . |
| Outcome: | The proposed method outperforms the expert model on 4 ScienceWorld tasks. |
Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression (2026.findings-acl)
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Jingyu Peng, Maolin Wang, Nan Wang, Jiatong Li, Yuchen Li, Yuyang Ye, Wanyu Wang, Pengyue Jia, Kai Zhang, Xiangyu Zhao
| Challenge: | Despite advances in aligning LLMs with human values, current safety mechanisms remain vulnerable to jailbreak attacks. |
| Approach: | They propose a black-box jailbreak method that uses logical expression translation to bypass LLM safety mechanisms. |
| Outcome: | The proposed method exploits the distributional gap between alignment data and logic-expressed inputs while preserving the underlying semantic intent and readability while evading safety constraints. |
Engage the Public: Poll Question Generation for Social Media Posts (2021.acl-long)
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| Challenge: | a novel application to generate poll questions for social media posts offers an easy way to hear the public's voice . for the silent majority, they tend to read others' messages instead of voicing their opinions with words . |
| Approach: | They propose to encode user comments and discover latent topics therein as contexts to generate poll questions for social media posts. |
| Outcome: | The proposed model outperforms popular models without exploiting topics from comments . human evaluations show it can generate high-quality polls useful to draw user engagements . |
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)
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| Challenge: | Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks. |
| Approach: | They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks. |
| Outcome: | The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings. |
Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Relation Triplet Extraction (2025.coling-main)
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| Challenge: | Existing methods to extract triplets from context often decompose into named entity recognition and relation classification, which may introduce error propagation. |
| Approach: | They propose a Relation-centric joint ZSRTE method which leverages unseen relation labels to extract triplets in one go. |
| Outcome: | The proposed method achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor. |
Dynamic Knowledge Distillation for Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Existing methods conduct knowledge distillation statically, e.g., student model aligns output distribution to teacher model on pre-defined training dataset. |
| Approach: | They propose a dynamic knowledge distillation that empowers the student to adjust the learning procedure according to its competency . they find it is promising and provide discussions on potential future directions towards more efficient methods . |
| Outcome: | The proposed method can boost student model performance while accelerating training . the proposed method reduces memory usage and accelerates model inference . |
Glider: Global and Local Instruction-Driven Expert Router (2025.emnlp-main)
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| Challenge: | Existing methods for routing-based expert models favor generalization over performance on held-in tasks. |
| Approach: | They propose a global and local instruction driven expert router that leverages recent LLMs' semantic reasoning capabilities to generate task-specific instructions from the input query. |
| Outcome: | The proposed method improves held-in performance while maintaining strong generalization on held-out tasks. |
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade (2021.findings-emnlp)
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| Challenge: | Experimental results show that CascadeBERT can achieve an overall 15% improvement under 4x speed-up compared with existing dynamic early exiting methods on six classification tasks. |
| Approach: | They propose a framework which emits predictions in internal layers without passing through the entire model. |
| Outcome: | The proposed framework can achieve 15% improvement under 4x speed-up compared with existing methods on six classification tasks yielding more calibrated and accurate predictions. |
EdgeInfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices (2025.acl-industry)
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| Challenge: | Existing KV cache optimizations struggle with irreversible token eviction in long-output tasks . alternative sequence modeling architectures prove costly to adopt within established Transformer infrastructures. |
| Approach: | They propose a memory-efficient solution for infinite contexts that integrates compressed memory into Transformer-based LLMs through a trainable memory-gating module. |
| Outcome: | The proposed solution achieves comparable performance to baseline Transformer-based LLMs while optimizing memory consumption and time to first token. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| Challenge: | Existing relation extraction methods focus on extracting intra-sentence relations for single entities. |
| Approach: | They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities . |
| Outcome: | The proposed dataset is the largest human-annotated dataset for document-level RE from plain text. |
Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)
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| Challenge: | Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse. |
| Approach: | They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets. |
| Outcome: | The proposed method is more effective than direct corpus concatenation and multi-task learning. |