Papers by Wei Zhou
Course-Correction: Safety Alignment Using Synthetic Preferences (2024.emnlp-industry)
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Rongwu Xu, Yishuo Cai, Zhenhong Zhou, Renjie Gu, Haiqin Weng, Liu Yan, Tianwei Zhang, Wei Xu, Han Qiu
| Challenge: | Recent studies show that large language models generate harmful content, but the potential for generating harmful content is an escalating concern. |
| Approach: | They propose to fine-tune LLMs with preference learning to emphasize the preference for timely course-correction by using an automated pipeline. |
| Outcome: | The proposed model improves course-correction skills without affecting general performance and resists jailbreak attacks. |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
Omni-RewardBench: Toward a Comprehensive Evaluation of Generative Reward Models Across Modalities (2026.acl-long)
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Chi-Min Chan, Yujin Zhou, Pengcheng Wen, Boqin Yin, Jiaming Ji, Juntao Dai, Wei Xue, Sirui Han, Yike Guo
| Challenge: | Existing evaluation benchmarks for ORMs are largely text-centric or limited to bimodal tasks . a new study examines the effectiveness of Omni-RewardBench for ORms across modalities . |
| Approach: | They propose a hybrid automatic-annotation and human-verification pipeline to construct high-quality evaluation data. |
| Outcome: | The proposed model is the first benchmark for comprehensive evaluation of ORMs across modalities. |
Alignment-Enhanced Decoding: Defending Jailbreaks via Token-Level Adaptive Refining of Probability Distributions (2024.emnlp-main)
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| Challenge: | Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures. |
| Approach: | They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues. |
| Outcome: | The proposed defense improves safety alignment while maintaining helpfulness. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards (2026.acl-long)
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| Challenge: | Large Language Models lack specialized priors for subtle grammatical distinctions, and Supervised Fine-Tuning fails to optimize for precision-focused metrics. |
| Approach: | They propose a framework that builds correction capability through Continual Pre-training on 5.9M balanced samples to internalize domain knowledge. |
| Outcome: | The proposed framework outperforms existing models on the NACGEC benchmark with 50.99 F0.5 and 57.17 precision while mitigating over-correction bias. |
The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing (2025.findings-acl)
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Xinwei Guo, Jiashi Gao, Junlei Zhou, Jiaxin Zhang, Guanhua Chen, Xiangyu Zhao, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei
| Challenge: | Large Language Models (LLMs) are increasingly integrated into our daily lives, raising ethical concerns, especially about perpetuating stereotypes. |
| Approach: | They propose a method that incorporates a neutral word semantics-based loss function to alleviate the deterioration of the LMS during debiasing. |
| Outcome: | The proposed method alleviates the deterioration of the Language Modeling Score (LMS) by incorporating a neutral word semantics-based loss function. |
Symbol tuning improves in-context learning in language models (2023.emnlp-main)
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Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc Le
| Challenge: | Language models are sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner. |
| Approach: | They propose to fine tune language models on in-context input-label pairs where natural language labels are replaced with arbitrary symbols. |
| Outcome: | The proposed model is much stronger at reasoning tasks and more robust to underspecified prompts than the standard model. |
Representation Learning with Conditional Information Flow Maximization (2024.acl-long)
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| Challenge: | Existing knowledge-theoretic representation learning frameworks are based on the information bottleneck principle, which preserves redundant features irrelevant to the given task. |
| Approach: | They propose a conditional information flow maximization framework to learn sufficient representations for the input data and target task by maximizing both input-representation and representation-label mutual information. |
| Outcome: | The proposed framework can extract noise-invariant sufficient representations for the input data and target task. |
v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound (2026.acl-long)
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Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Song-Chun Zhu, Bo Zhao, Zilong Zheng
| Challenge: | Humor enriches our daily lives and appears in many forms, from jokes and cartoons to comedies and viral videos. |
| Approach: | They introduce a video humor understanding benchmark to test their ability to understand humor from visual cues. |
| Outcome: | The proposed video humor understanding benchmark is based on a collection of short videos . it features rich annotations and a study of environmental sound that can enhance humor . |
Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods to detect fake news neglect a broader propagation uncertainty issue . Existing studies leverage the user interactions in a social media conversation thread to detect false news. |
| Approach: | They propose a dual graph-based model for improving fake news detection . they propose to explore latent interactions in the actual propagation . |
| Outcome: | The proposed model improves on two real-world datasets showing that it is superior to existing models. |
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. |
| Approach: | They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks. |
| Outcome: | The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. |
MentalSeek-Dx: Towards Progressive Hypothetico-Deductive Reasoning for Real-world Psychiatric Diagnosis (2026.acl-long)
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Xiao Sun, null Ymyang, Xinyi Jiang, Yu Tian, Junnan Zhu, Jiang Zhong, Qin Lei, Jingwang Huang, Haoyang Zeng, Xinyu Zhou, Xin Xiao, Kaiwen Wei
| Challenge: | Mental health disorders represent a burgeoning global public health challenge . lack of ecological validity and fine-grained diagnostic supervision limits their utility . |
| Approach: | They propose a medical-specialized LLM trained to internalize clinical reasoning process through supervised trajectory construction and curriculum-based reinforcement learning. |
| Outcome: | The proposed model achieves state-of-the-art with only 14B parameters, establishing a clinically grounded framework for reliable psychiatric diagnosis. |
Cross-Domain Fake News Detection based on Dual-Granularity Adversarial Training (2025.coling-main)
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| Challenge: | Existing approaches to detect fake news in unseen domains are limited by domain-specific training. |
| Approach: | They propose a cross-domain fake news detection method based on adversarial training . they use a document-level and entity-level model to generate domain-independent representations . |
| Outcome: | The proposed method can detect fake news in unseen domains with the help of pre-trained language models. |
From Lists to Emojis: How Format Bias Affects Model Alignment (2025.acl-long)
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| Challenge: | Format biases in reinforcement learning from human feedback are underexplored . despite its effectiveness, RLHF faces challenges, including policy and regulatory constraints . |
| Approach: | They extend the study of preference biases beyond verbosity bias to a wider range of format biase . they show that with a small amount of biased data, they can inject significant bias into the reward model . |
| Outcome: | The proposed approach can be easily exploited by large language models to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena. |
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs (2025.naacl-long)
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Yuhang Zhou, Giannis Karamanolakis, Victor Soto, Anna Rumshisky, Mayank Kulkarni, Furong Huang, Wei Ai, Jianhua Lu
| Challenge: | State-of-the-art methods for merging expert models with different architectures do not address parameter interference and require extensive fine-tuning to restore performance. |
| Approach: | They propose a method for merging experts with different architectures into a unified Mixture-of-Experts model with a goal of enhancing performance in each domain while retaining effectiveness on general tasks. |
| Outcome: | Experiments across multiple domains show that the proposed methods reduce fine-tuning costs and improve performance over state-of-the-art methods. |
BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training (2025.emnlp-main)
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| Challenge: | Recent studies highlight a fundamental dichotomy in deep learning optimization: parameter updates along the top eigendirections of the loss Hessian (Dom-space) capture most of the update magnitude, while updates in the orthogonal component (Bulk-space), have smaller magnitudes but drive most learning progress. |
| Approach: | They propose a plug-and-play framework that scales update components projected onto distinct subspaces and a block-wise strategy that applies this estimation on a per-parameter-block basis. |
| Outcome: | The proposed framework accelerates training by differentially scaling update components projected onto distinct subspaces, while enhancing stability by moderating updates in dominant subspace and boosting convergence speed by amplifying updates in bulk-space. |
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)
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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li
| Challenge: | Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion. |
| Approach: | They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree . |
| Outcome: | The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. |
“Yuki Gets Sushi, David Gets Steak?”: Uncovering Gender and Racial Biases in LLM-Based Meal Recommendations (2026.eacl-long)
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| Challenge: | Large language models inherit and amplify societal biases related to gender and race. |
| Approach: | They use a USChainMains dataset to evaluate group bias in Large Language Models . they found that LLMs recommend meals with higher levels of adverse nutrients for names associated with Black, Hispanic, or male individuals . |
| Outcome: | The proposed model scales improves overall recommendation healthfulness but is insufficient to eliminate the healthfulness gap between demographic groups. |
What’s Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews (2026.acl-long)
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| Challenge: | Existing efforts to detect factually incorrect content are omitted by creators who subtly reshape impressions by omitting crucial background context. |
| Approach: | They propose a multi-stage pipeline that simulates preview-based and context-based understanding and a OMGuard pipeline that combines interpretation-aware fine-tuning and rationale-guided misleading content correction. |
| Outcome: | The proposed framework lifts an 8B model’s detection accuracy to the level of a 235B LVLM while delivering stronger end-to-end correction. |
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. |
Pre-training for Abstractive Document Summarization by Reinstating Source Text (2020.emnlp-main)
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| Challenge: | Abstractive document summarization models are often trained on limited supervised data . authors present three objectives for pretraining abstractive summarizing models . |
| Approach: | They propose to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text. |
| Outcome: | The proposed method improves on two benchmark summarization datasets with 19GB of text . the goal is sentence reordering, next sentence generation and masked document generation . |
MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning (2025.findings-acl)
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Ke Wang, Junting Pan, Linda Wei, Aojun Zhou, Weikang Shi, Zimu Lu, Han Xiao, Yunqiao Yang, Houxing Ren, Mingjie Zhan, Hongsheng Li
| Challenge: | Large Language Models (LMMs) struggle with simple tasks such as geometry, e.g., arithmetic, and reasoning. |
| Approach: | They propose to leverage code as supervision for cross-modal alignment . they propose to use FigCodifier and ImgCode-8.6M to synthesize novel mathematical figures . |
| Outcome: | The proposed model surpasses GPT-4o and Claude 3.5 Sonnet in the geometry problem-solving subset of MathVista, achieving improvements of 8.9% and 9.2%. |
Learning by Analogy: Diverse Questions Generation in Math Word Problem (2023.findings-acl)
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| Challenge: | Existing methods for solving math word problem (MWP) use shortcut learning to train solvers based on samples with a single question. |
| Approach: | They propose to generate diverse yet consistent questions from a common scenario . they then feed the equations to a question generator to obtain the diverse questions . their method leads to performance improvement on the current benchmark Math23K . |
| Outcome: | The proposed method generates diverse yet consistent questions with a variety of equations and questions . it improves on the current benchmark, which is based on the proposed method . |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning (2020.coling-main)
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| Challenge: | Existing models for fake news detection are often insufficient or lacking in features . a novel structure-aware multi-head attention network can detect fake news in 4 hours . |
| Approach: | They propose a structure-aware multi-head attention network to detect fake news in mass news . they use credibility of publishers and users as prior weakly supervised information . |
| Outcome: | The proposed model can detect fake news in 4 hours with an accuracy of over 91% . the proposed model is faster than the state-of-the-art models . |
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)
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| Challenge: | Existing RC models focus on extractive or generative, but ignore integration of them. |
| Approach: | They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module. |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter. |
Pun2Pun: Benchmarking LLMs on Textual-Visual Chinese-English Pun Translation via Pragmatics Model and Linguistic Reasoning (2025.acl-srw)
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| Challenge: | Current approaches resort to suboptimal compromises and computational methods remain inadequate for translation. |
| Approach: | They propose a Constant-Variable Optimization (CVO) model for translation strategy and an Ovl metric for translation quality assessment that adapts to Chinese and English. |
| Outcome: | The proposed model improves performance on textual and visual puns while maintaining linguistic mechanisms and humorous effects. |
TableBank: Table Benchmark for Image-based Table Detection and Recognition (2020.lrec-1)
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| Challenge: | Existing techniques for table detection and recognition are limited to document types and layouts. |
| Approach: | They propose to build a table detection and recognition dataset with weak supervision from Word and Latex documents on the internet. |
| Outcome: | The proposed dataset contains 417K high quality labeled tables and is publicly available. |
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression (2021.emnlp-main)
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| Challenge: | Recent studies on compression of pretrained language models usually use preserved accuracy as the metric for evaluation. |
| Approach: | They propose two new metrics that measure how closely a compressed model mimics the original model. |
| Outcome: | The proposed metrics measure how closely a compressed model (i.e., student) mimics the original model (e.g., teacher). |
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
| Challenge: | Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text. |
| Approach: | They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types. |
| Outcome: | The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages. |
RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation (2026.acl-long)
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Sunzhu Li, Jiale Zhao, Huimin Ren, Zhenlin Wei, Yang Zhou, Jingwen Yang, Shunyu Liu, Kaike Zhang, Chen Wei
| Challenge: | Existing methods for generating open-ended rubrics suffer from scalability bottlenecks and coarse criteria resulting in a supervision ceiling effect. |
| Approach: | They propose a framework for automated Coarse-to-Fine Rubric Generation . their framework uses principle-guided synthesis, multi-model aggregation, difficulty evolution . |
| Outcome: | The proposed framework produces comprehensive and highly discriminative criteria capable of capturing the subtle nuances. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge (2021.naacl-main)
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| Challenge: | Cant is important for understanding advertising, comedies and dogwhistle politics . currently, there are very few resources available for the research of cant . |
| Approach: | They propose a large and diverse dataset for creating and understanding cant from a computational linguistics perspective. |
| Outcome: | The proposed dataset can be used to test word embedding similarity and pretrained language models. |
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025.emnlp-main)
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Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Guanbo Wang, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang
| Challenge: | Existing methods for fine-tuning-based compression suffer from verbose outputs, increasing computational overhead. |
| Approach: | They propose a framework to generate concise reasoning chains using Confidence Injection and Early Stopping. |
| Outcome: | The proposed framework reduces the length of the model by up to 50% while maintaining high task accuracy. |
FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph (2026.acl-long)
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| Challenge: | Equity research reports are crucial resources for investors, but lack professional analysis and the rapid evolution of market events outpaces their update cycles. |
| Approach: | They propose an event-Enhanced automated construction of financial knowledge graph (FinKario) that automatically integrates real-time company fundamentals and market events through prompt-driven extraction guided by professional institutional templates. |
| Outcome: | The proposed model outperforms financial LLMs by 18.81% and institutional strategies by 17.85% on average in backtesting. |
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)
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Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang
| Challenge: | Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand. |
| Approach: | They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages. |
| Outcome: | Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. |
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)
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| Challenge: | Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC). |
| Approach: | They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links . |
| Outcome: | The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty. |
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification (2024.acl-long)
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| Challenge: | Language models have demonstrated remarkable performance in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. |
| Approach: | They propose a method to assess concept bias in models during fine-tuning and in-context learning using ChatGPT. |
| Outcome: | The proposed method outperforms token removal approaches and is validated through extensive testing. |
Learning Dialogue Representations from Consecutive Utterances (2022.naacl-main)
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| Challenge: | Dialogue Sentence Embedding (DSE) is a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue-oriented tasks. |
| Approach: | They propose a self-supervised contrastive learning method that learns dialogue representations suitable for a wide range of dialogue tasks. |
| Outcome: | The proposed method outperforms baselines on five dialogue tasks on a few-shot and zero-shot datasets. |
Span-based Localizing Network for Natural Language Video Localization (2020.acl-main)
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| Challenge: | Existing approaches to NLVL are either ranking tasks or regressing the target video span. |
| Approach: | They propose a video span localizing network to solve a natural language video localization task using a span-based QA approach. |
| Outcome: | The proposed network outperforms the state-of-the-art methods on three benchmark datasets. |
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
| Approach: | They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications. |
| Outcome: | The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models . |
DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing (2026.findings-acl)
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Hongzhi Zhang, Yuanze Hu, Tinghai Zhang, Jia Fu, Tao Wang, Junwei Jing, Zhaoxin Fan, Wei Bi, Ruiming Tang, Han Li, Guorui Zhou, Kun Gai
| Challenge: | Large Language Models (LLMs) are evolving towards autonomous agents . retrieval capabilities are well-benchmarked, but post-retrieval synthesis is under-evaluated due to open-ended writing. |
| Approach: | They propose a benchmark to evaluate information consolidation capabilities using survey papers as gold standards. |
| Outcome: | The proposed benchmark analyzes the post-retrieval synthesis stage of large language models . it leverages high-quality survey papers as gold standards and reverse-engineers research requests . the proposed benchmark outperforms single-turn generation and reduces hallucinations . |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
| Approach: | They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio. |
| Outcome: | The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model . |
A Deep Relevance Model for Zero-Shot Document Filtering (P18-1)
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| Challenge: | Existing methods for document classification do not consider document filtering . existing methods do not include document filter. |
| Approach: | They propose a deep relevance model for zero-shot document filtering called DAZER . they use word embeddings to extract the relevance signals from word embeds . |
| Outcome: | The proposed model outperforms existing models on two document collections . it estimates the relevance between a document and a category by using seed words . |
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs (2024.emnlp-main)
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Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, but internal mechanisms that contribute to RAG’s effectiveness remain underexplored. |
| Approach: | They propose to examine the internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and examine their ability to improve RAG by examining expert activations. |
| Outcome: | The proposed method significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. |
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)
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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou
| Challenge: | Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. |
| Approach: | They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn . |
| Outcome: | The proposed benchmark is very challenging for state-of-the-art models, it is found. |
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)
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| Challenge: | Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information. |
| Approach: | They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets. |
Improving Alignment in LVLMs with Debiased Self-Judgment (2025.findings-emnlp)
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| Challenge: | Existing methods for aligning LVLMs rely on external datasets, human annotations or complex post-processing. |
| Approach: | They propose a method that generates a debiased self-judgment score for LVLMs . this self-evaluation metric is created internally by the model without external resources . |
| Outcome: | The proposed approach outperforms existing methods in reducing hallucinations and safety concerns. |
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations (2023.acl-long)
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| Challenge: | Existing methods to recognize emotions have limitations in discovering the intrinsic structure of data relevant to emotion labels, and struggle to extract generalized and robust representations. |
| Approach: | They propose a supervised adversarial contrastive learning framework for learning class-spread structured representations in a controlled manner. |
| Outcome: | The proposed framework can extract generalized and robust representations on three datasets and achieves state-of-the-art performance. |
Robust Learning for Multi-party Addressee Recognition with Discrete Addressee Codebook (2023.acl-short)
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| Challenge: | Existing addressee recognition models suffer from the issue of robustness when applied in real-world scenes. |
| Approach: | They propose a method which discretes addressees into a character codebook and makes it robust in a noisy environment. |
| Outcome: | The proposed method represents open set addressees and is robust even in noisy environments. |
Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias (2024.emnlp-main)
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| Challenge: | Existing prompting methods that require white-box access to the model or substantial training fail to simultaneously lessen toxicity and bias. |
| Approach: | They propose a strategy that encourages LLMs to integrate diverse human perspectives and self-regulate their responses by incorporating diverse human viewpoints. |
| Outcome: | The proposed approach can significantly diminish toxicity (up to 89%) and bias (up 73%) in LLMs’ responses. |
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification (D19-1)
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| Challenge: | Existing studies on adversarial attacks on deep learning models focus on generation of adversarials and defense against adversarial attacks. |
| Approach: | They propose a framework to identify and adjust malicious perturbations and block adversarial attacks for machine learning models. |
| Outcome: | The proposed framework outperforms baseline methods in blocking adversarial attacks for text classification models. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Multi-stream Information Fusion Framework for Emotional Support Conversation (2024.lrec-main)
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| Challenge: | Existing methods for ESC do not capture the dynamic transition of emotion intensity due to the difficulty to model its dynamic transition. |
| Approach: | They propose to fuse three streams for the effective modelling of emotion intensity using a multi-stream fusion unit. |
| Outcome: | The proposed model reduces the emotional distress of users with high-intensity of negative emotions by incorporating three different kinds of streams for the dynamic transition of emotion intensity. |
Leveraging Web-Crawled Data for High-Quality Fine-Tuning (2024.findings-emnlp)
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| Challenge: | Currently, large language models are fine-tuned using expensive human-annotated data or GPT-4 generated data. |
| Approach: | They propose to use web-crawled data to train a language model on a smaller set of data . their results show that the model can convert web data with irregular formats into high-quality ones . |
| Outcome: | The proposed model outperforms open-source models larger than 32B and outperformed open-sourced models such as GPT-3.5. |
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation. |
| Approach: | They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph. |
| Outcome: | The proposed model generates more informative, coherent, and natural responses than baseline models. |
AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
| Approach: | They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. |
| Outcome: | The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks. |
Beyond Sequences: Two-dimensional Representation and Dependency Encoding for Code Generation (2025.acl-long)
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| Challenge: | Existing code generation approaches represent code as a linear sequence of tokens, but positional encodings compromise generalization . explicit positional encoders sacrifice permutation invariance, imposes a strict order on the input sequence . |
| Approach: | They propose to represent code snippets as two-dimensional entities with explicit encodings . they propose to use dictionary learning to perform semantic matching between code lines . |
| Outcome: | The proposed model captures the hierarchical and spatial structure of code, especially the dependencies between code lines. |
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation (2024.findings-acl)
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Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong
| Challenge: | Modern large language models often "hallucinate" plausible but factually incorrect information, which reduces their trustworthiness especially in settings where accurate and up-to-date information is critical. |
| Approach: | They develop a human evaluation procedure to measure correctness and hallucination and use it to benchmark both closed and open-source LLMs. |
| Outcome: | The proposed method outperforms both competing search engine-augmented prompting methods and commercial systems on search-augmented QA. |
Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents (2026.acl-long)
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| Challenge: | Existing benchmarks focus on direct queries for a factual answer, but fail to evaluate the more crucial capability of actively applying memory to execute tasks. |
| Approach: | They propose a benchmark to evaluate whether agents can proactively leverage long-term memory to execute tool-based actions by selecting appropriate tools and grounding their parameters. |
| Outcome: | The proposed benchmarks show that 91.3% of tasks are memory-dependent . the benchmarks simulate persistent assistant usage, where users mention the same topic across long, interrupted interactions and expect previously established preferences and task states to be implicitly applied. |
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)
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| Challenge: | Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process. |
| Approach: | They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information. |
| Outcome: | The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA. |
Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)
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| Challenge: | Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors. |
| Approach: | They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. |
| Outcome: | Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods. |
MeaeQ: Mount Model Extraction Attacks with Efficient Queries (2023.emnlp-main)
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| Challenge: | Recent studies focus on limited-query budget settings and adopt random sampling or active learning-based sampling strategies on publicly available, unannotated data sources. |
| Approach: | They propose a model extraction attack with efficient Queries that uses a zero-shot sequence inference classifier to filter task-relevant data from a public text corpus instead of a problem domain-specific dataset. |
| Outcome: | The proposed method achieves higher similarity to the victim model than baselines while requiring fewer queries. |
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension (D19-58)
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| Challenge: | Experimental results show that unified model outperforms other models that treat encoding and matching separately. |
| Approach: | They evaluate a unified model with Transformer layers for machine reading comprehension . they find that the model learns different modeling strategies compared with previous models . |
| Outcome: | The unified model outperforms models with Transformer layers on the machine reading comprehension task. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
TabularMath: Understanding Math Reasoning over Tables with Large Language Models (2026.findings-acl)
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| Challenge: | Mathematical reasoning has long been a key benchmark for evaluating large language models. |
| Approach: | They propose a framework that transforms math word problems into scalable tabular reasoning tasks. |
| Outcome: | The proposed framework transforms math word problems into scalable and verified tabular reasoning tasks. |
Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection (2025.findings-acl)
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| Challenge: | Existing models for fake news detection capture domain-shared semantic features but fail to generalize well due to poor adaptability. |
| Approach: | They propose a framework to enable structure knowledge transfer between multiple domains . they compare content-only and propagation-rich data to preserve structural patterns . |
| Outcome: | The proposed framework can learn semantic and structural features across domains. |
A Unified Propagation Forest-based Framework for Fake News Detection (2022.coling-1)
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| Challenge: | Recent studies on fake news detection have focused on textual news material, but there is a lack of authoritative regulators. |
| Approach: | They propose a framework to explore latent correlations between propagation trees and a root-induced training strategy to encourage representations of propagation tree to be closer to their prototypical root nodes. |
| Outcome: | The proposed framework explores latent correlations between propagation trees to improve fake news detection. |
Improve Student’s Reasoning Generalizability through Cascading Decomposed CoTs Distillation (2024.emnlp-main)
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| Challenge: | Existing studies have tried to distill these capabilities into smaller language models (SLMs) however, these capabilities are often associated with more parameters, which is not practical to emergent in smaller models. |
| Approach: | They propose to decompose the traditional single-step learning process into two cascaded learning steps by restructuring the training objectives and concatenating the question with the rationale as input. |
| Outcome: | Extensive experiments show that the proposed method improves reasoning generalizability and diversity of the model. |
Video Dialog via Progressive Inference and Cross-Transformer (D19-1)
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| Challenge: | Existing visual dialog methods use RNN to encode the dialog history as a vector representation . a new method for video dialog is proposed, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous. |
| Approach: | They propose a method which progressively updates query information based on dialog history and video content until the agent thinks it is sufficient and unambiguous. |
| Outcome: | The proposed method can be used to infer video dialog answers on large-scale datasets. |
CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)
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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu
| Challenge: | Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints. |
| Approach: | They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs. |
| Outcome: | The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications. |
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)
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| Challenge: | Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems. |
| Approach: | They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance. |
| Outcome: | The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist. |
Learning Gender-Neutral Word Embeddings (D18-1)
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| Challenge: | Word embeddings trained on human-generated corpora inherit strong gender stereotypes . prior studies show such embeddables exhibit social biases, such as gender stereotype . |
| Approach: | They propose a method to preserve gender information in certain dimensions of word vectors . they propose GN-GloVe, which is a gender-neutral variant of the word embedding model . |
| Outcome: | The proposed method preserves gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence. |
Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models (2026.acl-long)
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| Challenge: | a recent study explores efficient ultra-long context modeling. |
| Approach: | They propose to use Hierarchical Sparse Attention to achieve efficient ultra-long context modeling. |
| Outcome: | The proposed model performs comparable to full-attention baselines on in-domain and out-of-domain tasks. |
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks. |
| Approach: | They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity. |
| Outcome: | The proposed framework improves the robustness of pre-trained language models and alleviates performance degradation under adversarial attacks. |
Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering (2025.findings-naacl)
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| Challenge: | Recent studies on complex table question answering focus more and more on complex instances, as they are ubiquitous in table data analysis. |
| Approach: | They propose a framework that requires neither fine-tuning nor closed-source models to solve complex table question answering (TQA) their framework outperforms previous SoTA systems on three out of four benchmarks and performs comparably to the larger and more expensive closed-sourced model GPT-4 on two benchmarks. |
| Outcome: | The proposed framework outperforms closed-source models and closed-sourced models on three out of four benchmarks and performs comparable to the larger and more expensive closed-Source model GPT-4 on two benchmarks. |
Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)
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Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Yang, Tingting Gao, Guorui Zhou
| Challenge: | Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning. |
| Approach: | They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning. |
| Outcome: | The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness. |
Verb Metaphor Detection via Contextual Relation Learning (2021.acl-long)
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| Challenge: | Recent work on verb metaphor detection focuses on analyzing restricted forms of linguistic context. |
| Approach: | They propose a model which explicitly models the relation between a verb and its various contexts. |
| Outcome: | The proposed model gets competitive results compared with state-of-the-art approaches on the VUA, MOH-X and TroFi datasets. |
FlashAudio: Rectified Flow for Fast and High-Fidelity Text-to-Audio Generation (2025.acl-long)
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| Challenge: | Recent advances in latent diffusion models (LDMs) have markedly enhanced text-to-audio generation, yet their iterative sampling processes impose substantial computational demands, limiting practical deployment. |
| Approach: | They propose to learn straight flow for fast simulation by using flashAudio with rectified flows and immiscible flow to minimize the total distance of data-noise pairs in a batch vias assignment. |
| Outcome: | The proposed method can learn straight flow for fast simulations and reduce noise distribution. |
Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering (2025.findings-acl)
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| Challenge: | Existing studies on table question answering use either serialized text or images . lack of controlled setups limits fine-grained distinctions between these approaches. |
| Approach: | They propose a method selecting table representations dynamically and compare their performance to previous studies. |
| Outcome: | The proposed method improves performance 10% compared to using both representations indiscriminately. |
Capture the Key in Reasoning to Enhance CoT Distillation Generalization (2025.acl-long)
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| Challenge: | Existing distillation methods for Large Language Models (LLMs) focus on fine-tuning student SLMs on correct data, resulting in students struggling to learn the key instead of analyzing mistakes according to correct solutions. |
| Approach: | They propose a method that exposes key reasoning steps rather than simple fine-tuning students' CoTs data by using a set of prompts with similar reasoning paths but divergent conclusions. |
| Outcome: | The proposed method improves student SLMs' ability to learn key reasoning steps rather than fine-tuning them on teacher data. |
AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation (2026.acl-long)
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Rui Qian, Chuanhang Deng, Qiang Huang, Jian Xiong, Mingxuan Li, Yingbo Zhou, Wei Zhai, Jintao Chen, Dejing Dou
| Challenge: | Existing models rely on a single segmentation token whose hidden state implicitly encodes both semantic reasoning and spatial localization . Existing methods rely only on SEG>, which encodes semantic reasoning, limiting the model's ability to explicitly disentangle what to segment from where to segment. |
| Approach: | They propose a method which reformulates reasoning segmentation as a structured conditional generation process over image tokens conditioned on language grounded query banks. |
| Outcome: | The proposed model bridges token-level predictions and pixel-level supervision by decoupling spatial grounding from semantic reasoning through structured language grounded query banks. |
GAPO: Robust Advantage Estimation for Real-World Code LLMs (2026.findings-acl)
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Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong, Hong Wang, Chenxing Wei, Yuyan Zhou, Yubin Qi, Qiang Lin, Jian Cao
| Challenge: | Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, but in real-world code editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted advantage computation and increased rollout outliers. |
| Approach: | They propose a group-relative method that finds an interval with the highest SNR and uses the median of that interval as an adaptive Q to replace the group mean in advantage calculation. |
| Outcome: | The proposed method improves on nine instruction-tuned LLMs while remaining plug-and-play and efficient. |
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)
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Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani
| Challenge: | Existing scaling of language models is expensive and requires significant computational costs. |
| Approach: | They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
| Outcome: | The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
CROP: Contextual Region-Oriented Visual Token Pruning (2025.emnlp-main)
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| Challenge: | Existing VLMs process entire images, leading to excessive visual tokens . redundant image information also introduces a large number of visual token, requiring much higher memory and computation in VLM. |
| Approach: | They propose a framework to prune visual tokens using localization and pruning . they propose CROP to locate local image regions relevant to the query . |
| Outcome: | The proposed framework outperforms existing visual token pruning methods on a wide range of tasks. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)
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| Challenge: | Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands. |
| Approach: | They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence . |
| Outcome: | The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence. |
Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation (2026.acl-long)
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| Challenge: | Table Question Answering (TQA) aims to answer natural language questions using tabular data. |
| Approach: | They propose a systematic overview of TQA research using large language models and summarize available benchmarks based on task features. |
| Outcome: | The proposed framework provides a comprehensive overview of the current state of the art in the field of Table Question Answering. |
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization (2020.coling-main)
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| Challenge: | Existing studies have shown that extracting sentences at sentence level is not the best solution for document summarization. |
| Approach: | They propose to extract sub-sentential units based on the constituency parsing tree and a neural extractive model which leverages the sub-sensential information and extracts them. |
| Outcome: | The proposed model performs competitively compared to full sentence extraction under automatic and human evaluations. |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
Neural Open Information Extraction (P18-2)
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| Challenge: | Existing Open IE systems are built on hand-crafted patterns from syntactic parsing, yet they face errors in propagation and compounding at each stage. |
| Approach: | They propose a neural Open IE approach with an encoder-decoder framework . they propose to learn highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open ie system. |
| Outcome: | The proposed approach outperforms baseline methods while maintaining comparable computational efficiency. |
Exploring the Effectiveness of Multi-Lingual Commonsense Knowledge-Aware Open-Domain Dialogue Response Generation (2023.findings-emnlp)
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| Challenge: | Existing studies have shown that commonsense knowledge-aware models can improve informativeness while reducing the hallucination issue. |
| Approach: | They propose a task to use commonsense knowledge in other languages to enhance the current dialogue generation by using commonsensical knowledge in different languages. |
| Outcome: | The proposed model improves the current dialogue generation while reducing the hallucination issue. |
ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)
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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu
| Challenge: | Existing studies focus on language-based premises and deduce valid conclusions from visual observations. |
| Approach: | They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning . |
| Outcome: | Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts . |
CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions (2022.coling-1)
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| Challenge: | Existing studies on story generation focus on coarse-grained control of the story, neglecting the details of the narrative. |
| Approach: | They propose a model for fine-grained control on the story that allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned. |
| Outcome: | The proposed method has strong controllability to generate customized stories according to the fine-grained personalized guidance. |
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)
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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). |
| Approach: | a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance. |
| Outcome: | a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models . |
LeLoRA: Learnable Low-Rank Adaptation of Large Language Models (2026.acl-long)
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| Challenge: | Existing approaches to fine-tuning large language models (LLMs) rely on manually specified and fixed hyperparameters, resulting in suboptimal performance and low parameter efficiency. |
| Approach: | They propose a framework that allows for dynamically learned adaptive adaptation strategies to be used to fine-tune large language models. |
| Outcome: | The proposed framework outperforms baselines in adapting large language models. |
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020.emnlp-main)
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| Challenge: | Existing methods to improve the efficiency of GEC are not efficient enough for GEC. |
| Approach: | They propose a language-independent approach to improve the efficiency of GEC by dividing the task into two subtasks: ESD and ESC. |
| Outcome: | The proposed approach performs comparably to conventional seq2seq approaches in English and Chinese GEC benchmarks with less than 50% time cost for inference. |
Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations. |
| Approach: | They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules . |
| Outcome: | Extensive experiments show that the proposed model improves relation extraction. |
Neural Latent Extractive Document Summarization (D18-1)
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| Challenge: | Existing summarization paradigms focus on extractive summarizing based on sentence level labels . |
| Approach: | They propose a latent variable extractive model where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. |
| Outcome: | The proposed model outperforms a strong extractive baseline trained on rule-based labels and performs competitively with several recent models. |
Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)
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| Challenge: | Large language models (LLMs) rely on English data for training, but are often not comparable across other languages. |
| Approach: | They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness . |
| Outcome: | The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination. |
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (P19-1)
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| Challenge: | Neural extractive summarization models employ hierarchical encoders with inaccurate sentence-level labels. |
| Approach: | They propose a method to pre-train a hierarchical encoder with unlabeled data. |
| Outcome: | The proposed model outperforms its initialized counterpart by 1.25 ROUGE on CNN and 2.0 ROUGEE on a version of New York Times dataset. |
SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (2022.emnlp-main)
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| Challenge: | Existing models for pre-training text and speech are based on unlabeled audio data. |
| Approach: | They propose a unified-modal speech-unit-text pre-training model that connects speech encoders and text decoders with a shared unit encoder. |
| Outcome: | The proposed model improves on automatic speech recognition and speech translation tasks and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks. |
Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs (2026.acl-long)
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Tingchao Fu, Wenkai Wang, Fanxiao Li, Huadong Zhang, Jinhong Zhang, Dayang Li, Yunyun Dong, Renyang Liu, Wei Zhou
| Challenge: | Existing knowledge editing paradigms suffer from editing decoupling failures . entity knowledge is sequestered into disentangled modality-specific pathways . |
| Approach: | They propose a method that explicitly disentangles and localizes modality-specific neuron groups for targeted knowledge. |
| Outcome: | The proposed method outperforms baselines in reliability and consistency while preserving model locality. |
IMRRF: Integrating Multi-Source Retrieval and Redundancy Filtering for LLM-based Fake News Detection (2025.naacl-long)
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| Challenge: | Existing methods to detect fake news rely on manual checking, which is time-consuming. |
| Approach: | They propose a model which integrates textual corpus retrieval with knowledge graph retrieval to retrieve more comprehensive evidence and a redundant information filtering strategy which minimizes the influence of irrelevant information on the LLM reasoning process. |
| Outcome: | The proposed method outperforms state-of-the-art fact-checking baselines on two challenging fact- checking datasets. |
Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)
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| Challenge: | Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning. |
| Approach: | They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations. |
| Outcome: | The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs. |
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)
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Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou
| Challenge: | Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks. |
| Approach: | They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets . |
| Outcome: | The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages. |
ToViLaG: Your Visual-Language Generative Model is Also An Evildoer (2023.emnlp-main)
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| Challenge: | Recent large-scale Visual-Language Generative Models (VLGMs) generate toxic content, e.g., offensive text and pornography images, raising significant ethical risks. |
| Approach: | They propose a bottleneck-based detoxification method to reduce toxicity while maintaining comparable generation quality. |
| Outcome: | The proposed method could reduce toxicity while maintaining comparable generation quality. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou
| Challenge: | Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks. |
| Approach: | They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework. |
| Outcome: | The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks. |
Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)
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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang
| Challenge: | Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials. |
| Approach: | They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located. |
| Outcome: | The proposed method outperforms previous detection methods by large margins on three text classification tasks. |
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 . |
Structure-aware Domain Knowledge Injection for Large Language Models (2025.acl-long)
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| Challenge: | Structure-aware Continual Pre-Training (SCPT) and Structure-Aware Supervised Fine-Tuning (SSFT) are two-stage strategies for knowledge injection and alignment that reduces the training corpus needs to 5% while achieving 100% of traditional knowledge injection performance. |
| Approach: | They propose a method to efficiently transform foundation Large Language Models into domain specialists by using two-stage strategies: Structure-aware Continual Pre-Training and Structure-Aware Supervised Fine-Tuning. |
| Outcome: | The proposed method significantly reduces the training corpus needs to a mere 5% while achieving 100% of traditional knowledge injection performance. |
AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)
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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin
| Challenge: | Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora . |
| Approach: | They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process . |
| Outcome: | The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query . |
Entity-Aware Abstractive Multi-Document Summarization (2021.findings-acl)
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| Challenge: | Existing models for multidocument summarization do not focus on explicitly modeling the underlying semantic information across documents. |
| Approach: | They propose an entityaware model for abstractive multi-document summarization that augments the classical Transformer-based encoder-decoder framework with a heterogeneous graph consisting of text units and entities as nodes. |
| Outcome: | The proposed model can deal with saliency and redundancy issues explicitly and can be used with pre-trained language models, arriving at improved performance. |
DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)
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| Challenge: | Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information. |
| Approach: | They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation . |
| Outcome: | The proposed model trains on DocBank accurately recognize layout information for a variety of documents. |
Challenging distributional models with a conceptual network of philosophical terms (2021.naacl-main)
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| Challenge: | Existing methods for analyzing philosophical data are not accurate enough to support philosophers . comparative research on concepts should follow a conceptual model approach, authors argue . |
| Approach: | They propose a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. |
| Outcome: | The proposed model does not perform well enough to directly support philosophers yet, but it yields promising directions for future work. |
Visualization Recommendation with Prompt-based Reprogramming of Large Language Models (2024.acl-long)
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| Challenge: | Traditional visualization recommendations require extensive manual maintenance and yet fail to fully comprehend tabular data. |
| Approach: | They propose a hierarchical table prompt-based reprogramming framework that integrates tabular data into LLMs through a strategically crafted prompt learning method. |
| Outcome: | The proposed framework achieves state-of-the-art performance and will be made publicly available upon acceptance. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
Cogito: A Cognitive Agentic Framework Driven by Dynamic Graph of Thoughts for Financial Report Generation (2026.findings-acl)
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| Challenge: | Existing approaches to financial report generation are insufficient to handle dynamic uncertainties of real-world financial environments. |
| Approach: | They propose a cognitively grounded agentic framework for professional financial report generation that is driven by Dynamic Graph of Thoughts and a social collaboration mechanism to facilitate coordinated agent interaction. |
| Outcome: | The proposed framework is based on a dynamic reasoning model and social collaboration mechanism. |
Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)
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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values. |
| Approach: | They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values . |
| Outcome: | The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompts (2023.findings-emnlp)
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| Challenge: | Continual pre-training has been used for a multitude of domains and tasks . a continually pre-trained model can show a non-decreasing performance on unseen domains . |
| Approach: | They propose a method that generates domain-specific prompts by agreement and disagreement losses. |
| Outcome: | The proposed method achieves improvements of 3.57% and 3.4% on two real-world datasets. |
Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)
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| Challenge: | Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available. |
| Approach: | They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities . |
| Outcome: | The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text. |
Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (2021.acl-long)
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| Challenge: | Existing studies on rumor detection focus on text content and propagation structure . however, the uncertainty caused by unreliable relations in propagation structures is common . |
| Approach: | They propose a Bayesian-based model that captures propagation uncertainty for rumor detection. |
| Outcome: | The proposed model achieves better performance than baseline methods on rumor detection and early rumour detection tasks. |
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. |
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)
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Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou, Yining Hua, Grace Ziqi Zhou, Haiyang Zhang, Tao Shen, Wei Wang, John Torous, Shaoxiong Ji, Ling Chen
| Challenge: | Psychological defenses are strategies people use to manage distress. |
| Approach: | They propose a dialogue corpus with help seeker utterances labeled for defense level and a DMRS Co-Pilot pipeline that provides evidence-based pre-annotations. |
| Outcome: | The proposed framework reduces annotation time by 24.0% in a counterbalanced study. |
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability (2023.emnlp-main)
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| Challenge: | Neural network models are vulnerable to adversarial examples, and current methods based on adversarially transferable models rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model’s structural details. |
| Approach: | They propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks. |
| Outcome: | The proposed approach achieves superior attack performance with small cost on ten datasets and demonstrates that it is a novel approach. |
Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)
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| Challenge: | Existing work performs code repair and commit message generation independently. |
| Approach: | They propose a cascaded method to repair program codes and generate commit messages in a unified framework. |
| Outcome: | The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset. |
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)
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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou
| Challenge: | Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected. |
| Approach: | They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models. |
| Outcome: | The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models. |
DORA: Dynamic Optimization Prompt for Continuous Reflection of LLM-based Agent (2025.coling-main)
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| Challenge: | Existing studies have shown that reflection can enhance performance, but our investigation reveals an undesirable pattern in reflection framework: effective self-reflection occurs primarily at the beginning of iterations, with subsequent attempts failing to produce further improvements. |
| Approach: | They propose a framework that generates task-adaptive reflection advice using an external open-source small language model. |
| Outcome: | The proposed framework generates task-adaptive and diverse reflection advice in MiniWoB++ and Alfworld environments. |
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption (2022.findings-acl)
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Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, Furu Wei
| Challenge: | enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks. |
| Approach: | They propose an approximation approach for transformers which enables inference on ciphertext data. |
| Outcome: | The proposed approach can infer pre-trained models on encrypted data with negligible performance drop but enjoy theory-guaranteed privacy-preserving advantage. |
Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods rely on sparse outcome rewards, which fail to credit correct intermediate steps in partially successful solutions. |
| Approach: | They propose a process reward model that rewards correct steps only when they detect errors . they propose VPPO, which rewards the correct prefix and an erroneous suffix . |
| Outcome: | a new approach outperforms sparse-reward RL and prior PRM-guided baselines on Pass@1 and Pass@K . a process reward model (PRM) outperformed sparser-rebound RL on multiple reasoning benchmarks . |
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots (P18-2)
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| Challenge: | Existing methods to learn matching models for retrieval-based chatbots are lacking. |
| Approach: | They propose a method that uses a sequence-to-sequence architecture model as a weak annotator to judge the matching degree of unlabeled pairs and performs learning with both the weak signals and the unlabed data. |
| Outcome: | The proposed method improves on two public data sets on matching models on retrieval-based chatbots. |
Grammar-Based Patches Generation for Automated Program Repair (2021.findings-acl)
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| Challenge: | Automated program repair (APR) aims to find an automatic solution to program language bugs without human intervention. |
| Approach: | They propose a grammar-based rule-rule model which regards the repair process as the transformation of grammar rules and employs a tree-based self-attention approach to guarantee grammar correctness. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a Java dataset in terms of generated code accuracy. |
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)
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| Challenge: | Existing methods for enhancing the performance of large language models require expensive manual annotations. |
| Approach: | They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence. |
| Outcome: | The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method. |
Large Language and Protein Assistant for Protein-Protein Interactions Prediction (2025.acl-long)
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Peng Zhou, Pengsen Ma, Jianmin Wang, Xibao Cai, Haitao Huang, Wei Liu, Longyue Wang, Lai Hou Tim, Xiangxiang Zeng
| Challenge: | Existing methods for predicting protein-protein interactions oversimplify the problem of PPI prediction in a semi-supervised manner. |
| Approach: | They propose a multimodal large language model that integrates proteins and PPI networks. |
| Outcome: | Experiments show that LLaPA can predict protein-protein interactions (mPPI) types and affinities based on sequence data. |
CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection (2025.findings-acl)
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| Challenge: | Multimodal large language models have demonstrated impressive capabilities in visual reasoning and text generation. |
| Approach: | They propose a multimodal large language model that captures deeper relationships between images and text . they propose CMIE, which uses a Coexistence Relationship Generation strategy and an AS mechanism to detect misinformation. |
| Outcome: | The proposed framework outperforms existing methods in detecting out-of-context misinformation. |
Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges (2025.findings-acl)
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| Challenge: | Privacy risks in text-only Large Language Models are well-documented, especially their tendency to memorize and leak sensitive information. |
| Approach: | They propose a dataset to assess privacy risks across multi-modal tasks and scenarios . they demonstrate how models leak sensitive data across various tasks . |
| Outcome: | The proposed model can leak sensitive data embedded in images or stored in memory, exposing privacy risks. |
V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)
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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
| Approach: | They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis. |
| Outcome: | The proposed framework assesses code generation and visual game generation using a sandbox environment. |
LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)
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Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin
| Challenge: | Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies. |
| Approach: | They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation. |
| Outcome: | The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios . |
Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)
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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao
| Challenge: | Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception. |
| Approach: | They propose a framework that organizes audio information into three explicit components in a unified JSON format. |
| Outcome: | The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |
Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples. |
| Approach: | They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search. |
| Outcome: | The proposed method improves on previous work on adversarial robustness evaluation. |
MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples (2025.coling-main)
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Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng
| Challenge: | Existing preference optimization methods such as DPO and KTO are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data. |
| Approach: | They propose an algorithm that leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data. |
| Outcome: | The proposed algorithm outperforms DPO, ORPO, and SimPO on MT-Bench and Arena-Hard. |
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs (2026.findings-eacl)
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Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan
| Challenge: | Large Language Models (LLMs) can generate code from natural language queries, but runtime code generation is limited due to unverified code, security risks, longer response times, and higher computational costs. |
| Approach: | They propose an offline simulation framework to curate a software-specific skillset by exploiting large language models and publicly available scripting guides. |
| Outcome: | The proposed framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. |
From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens (2025.emnlp-demos)
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Hala Sheta, Eric Haoran Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, Freda Shi
| Challenge: | Existing vision-language models are based on exactmatch based accuracy and its derivations to evaluate performance. |
| Approach: | They propose a toolkit that supports systematic benchmarking, analysis, and interpretation of vision-language models by extracting intermediate outputs from any layer during the forward pass of open-source VLMs. |
| Outcome: | The proposed toolkit supports 16 state-of-the-art base VLMs and their over 30 variants and is extensible to accommodate new models without changing the core logic. |
Uncovering Argumentative Flow: A Question-Focus Discourse Structuring Framework (2025.emnlp-main)
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| Challenge: | Existing structure modeling approaches fail to capture the author’s rhetorical intent and reasoning process. |
| Approach: | They propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question and a set of attentional foci. |
| Outcome: | The proposed framework outperforms baseline models and curated models on an argument reconstruction task in Chinese think-tank articles and claims coverage. |
Learning from LLM Agents: In-Context Generative Models for Text Casing in E-Commerce Ads (2025.emnlp-industry)
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| Challenge: | Existing NER-based transformer models are expensive and lack contextual dependencies, making them less reliable when handling unseen or ad-specific terms, e.g., brand names. |
| Approach: | They propose a two-stage approach to casing correction in e-commerce ad content that leverages Chain-of-Actions to enforce content policies while accurately handling ads-specific terms. |
| Outcome: | The proposed model outperforms existing NER-based models and achieves near-LLM performance at a fraction of the cost. |
BERT-of-Theseus: Compressing BERT by Progressive Module Replacing (2020.emnlp-main)
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| Challenge: | a novel approach to compress neural networks by progressive module replacement is proposed . a number of techniques have been proposed to compress pretraining and fine-tuning models . |
| Approach: | They propose a model compression approach that divides BERT into modules and builds their compact substitutes. |
| Outcome: | The proposed approach outperforms existing knowledge distillation approaches on GLUE benchmark . it is based on a model that divides the original BERT into several modules and builds their substitutes . |
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2024.acl-long)
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Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou
| Challenge: | supervised fine-tuning (SFT) is a technique used to enhance multiple abilities in large language models. |
| Approach: | They propose to study the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during supervised fine-tuning. |
| Outcome: | The proposed model improves math reasoning and code generation with increasing data amount . the proposed model size and SFT strategies can be used to learn multiple skills with different scaling patterns. |
Parallel Attention Network with Sequence Matching for Video Grounding (2021.findings-acl)
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| Challenge: | Existing approaches to video grounding are sensitive to quality of proposals and inefficient because all proposal-query pairs are compared. |
| Approach: | They propose a Parallel Attention Network with Sequence matching to capture selfmodal contexts and cross-modal attentive information between video and text. |
| Outcome: | The proposed approach is superior to state-of-the-art methods on three datasets. |
Is the Answer in the Text? Challenging ChatGPT with Evidence Retrieval from Instructive Text (2023.findings-emnlp)
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| Challenge: | Generative language models have recently shown remarkable success in generating answers to questions in a given textual context, but they suffer from hallucination, wrongly cite evidence, and spread misleading information. |
| Approach: | They propose a benchmark to evaluate an annotated WikiHow article and use it to retrieve answers to questions from trustworthy texts. |
| Outcome: | The proposed model can retrieve answers to lexically varied and open-ended questions from trustworthy instructive texts. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents. |
| Approach: | They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents. |
| Outcome: | The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions. |
SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling (2025.findings-acl)
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| Challenge: | Traditional sentiment analysis methods focus on static reviews, failing to capture temporal relationship between user sentiment rating and textual content. |
| Approach: | They propose a dynamic graph-based framework that addresses data sparsity in streaming reviews. |
| Outcome: | The proposed framework reduces data sparsity by categorizing users into mid-tail, long-tail and extreme scenarios and incorporating LLM enhancements within a dynamic graph-based structure. |
Do Transformer Modifications Transfer Across Implementations and Applications? (2021.emnlp-main)
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Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel
| Challenge: | Currently, the Transformer is the de facto architecture of choice for processing sequential data. |
| Approach: | They evaluate the Transformer architecture and its modifications in a shared experimental setting . they conjecture that performance improvements may strongly depend on implementation details . |
| Outcome: | The proposed improvements do not significantly improve performance, the authors find . the proposed improvements are either developed in the same codebase or are minor changes . |
A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity (2024.naacl-long)
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Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, Daphne Ippolito
| Challenge: | a large number of pretraining data design practices are under-documented, authors say . authors: strong performance of modern language models depends on selfsupervised pretraining . |
| Approach: | They propose to pretrain models on data curated at different collection times . they find temporal shift between evaluation data and pretraining data leads to performance degradation . |
| Outcome: | The results validate, quantify, and expose many undocumented intuitions about text pretraining . authors say this practice has outperformed other models in the field . |
Unsupervised Fine-tuning for Text Clustering (2020.coling-main)
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| Challenge: | Existing approaches to text clustering fine-tune pre-trained models have been limited. |
| Approach: | They propose a method to fine-tune pre-trained models unsupervisedly for text clustering by learning text representations and cluster assignments using a clustering oriented loss. |
| Outcome: | The proposed model outperforms baseline methods and achieves state-of-the-art results on three text clustering datasets. |
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study (P19-1)
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| Challenge: | Sequence-to-sequence (seq2sequ) models have a weakness: they cannot always generate sentences without grammatical errors. |
| Approach: | They propose to use automatic grammatical error correction to improve seq2seq models . they conduct experiments on machine translation, formality style transfer, sentence compression and simplification . |
| Outcome: | The proposed system can improve grammaticality of generated text and improve formal style tasks. |
SATBench: Benchmarking LLMs’ Logical Reasoning via Automated Puzzle Generation from SAT Formulas (2025.emnlp-main)
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Anjiang Wei, Yuheng Wu, Yingjia Wan, Tarun Suresh, Huanmi Tan, Zhanke Zhou, Sanmi Koyejo, Ke Wang, Alex Aiken
| Challenge: | SATBench is a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SAT) problems. |
| Approach: | They propose a benchmark to evaluate logical reasoning capabilities of large language models (LLMs) using logical puzzles derived from Boolean satisfiability problems. |
| Outcome: | The proposed model achieves 65.0% accuracy on hard UNSAT problems, close to the baseline of 50%. |
LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search (2026.findings-acl)
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| Challenge: | Existing methods for enhancing inductive reasoning of large language models often lack explicit optimization guidance and effective error correction. |
| Approach: | They propose a plug-and-play test-time framework that integrates multi-agent coordination with Monte Carlo Tree Search to improve inductive reasoning. |
| Outcome: | The proposed framework outperforms existing methods on four benchmarks and shows consistent improvements on QWQ-32B and Deepseek-V3 . |
Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting (2021.emnlp-main)
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| Challenge: | Existing text infilling objectives for pretrained language models require self-supervision by masking out tokens or spans in text. |
| Approach: | They propose to extend text infilling to a self-supervised sequence-to-sequence (Seq2Sequen) task. |
| Outcome: | The proposed task improves the model's performance on various natural language generation tasks. |
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2023.findings-acl)
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Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, Jason Wei
| Challenge: | Language models have already made good progress on this benchmark, with the best model outperforming average reported human-rater results on 65% of the BIG-Bench tasks. |
| Approach: | They propose to use chain-of-thought prompting to challenge language models on 23 challenging BIG-Bench tasks which they call BIG-Bench Hard. |
| Outcome: | The proposed language models outperform the average human-rater on 65% of the BIG-Bench tasks. |
HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)
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| Challenge: | relying on large language models for information has raised concerns about reliability and accuracy of outputs. |
| Approach: | They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process. |
| Outcome: | The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility. |
Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)
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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang
| Challenge: | Existing methods for training effective AI agents often resort to synthetic data generation. |
| Approach: | They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance . |
| Outcome: | The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset. |
Vision Language Model Helps Private Information De-Identification in Vision Data (2025.findings-acl)
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| Challenge: | Visual Language Models (VLMs) have gained popularity due to their ability to solve imagerelated tasks. |
| Approach: | They propose a framework to enhance privacy awareness of visual language models . they use a specialized instruction-tuning dataset and a tailored training methodology . |
| Outcome: | The proposed framework outperforms existing approaches in handling private information. |
Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)
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| Challenge: | propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework . |
| Approach: | They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation. |
| Outcome: | The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns. |
RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)
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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang
| Challenge: | Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks. |
| Approach: | They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. |
| Outcome: | The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)
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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
| Challenge: | XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios . |
| Approach: | They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora. |
| Outcome: | The proposed dataset is labeled in English and includes only natural language understanding tasks. |
SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation (2021.acl-long)
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| Challenge: | Using pre-training methods for NMT models is difficult because of the cross-attention module . cross-linguistic embeddings are not used to pretrain a decoder . |
| Approach: | They propose a semantic interface between pre-trained encoder and pre-train decoder to improve NMT performance. |
| Outcome: | The proposed method improves on translation and unsupervised translation tasks. |
Tell2Design: A Dataset for Language-Guided Floor Plan Generation (2023.acl-long)
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| Challenge: | Recent studies have demonstrated impressive results in generating high-fidelity artistic images. |
| Approach: | They propose a Sequence-to-Sequence model that can serve as a strong baseline for future research. |
| Outcome: | The proposed model can be used as a baseline for future research and human evaluations are conducted on the generated samples and provided an analysis of human performance. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |
Enhancing In-Context Learning via Implicit Demonstration Augmentation (2024.acl-long)
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| Challenge: | In-context learning (ICL) is a new paradigm for pre-trained language models that can make predictions for unseen inputs without updating parameters. |
| Approach: | They propose a method that enables a model to augmented copies of a demonstration by leveraging their deep feature distribution and a logit calibration mechanism. |
| Outcome: | The proposed method significantly improves the average and worst-case accuracy across diverse PLMs and tasks. |
AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)
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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo
| Challenge: | Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking. |
| Approach: | They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation. |
| Outcome: | The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts. |
Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement (2026.acl-long)
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| Challenge: | Existing methods to learn internal world models rely on one-step supervision . however, standard MTP suffers from structural hallucinations . |
| Approach: | They propose a method which anchors predictions to ground-truth hidden state trajectories. |
| Outcome: | The proposed method bridges the gap between discrete tokens and continuous state representations, reducing structural hallucinations, and improving robustness to perturbations. |
Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference (2025.acl-long)
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| Challenge: | Existing Large Vision-Language Models (LVLMs) learn visual capacity through visual instruction tuning. |
| Approach: | They propose a method for LVLMs to be trained by selective layers tuning . they propose removing non-critical layers outside the visual region . |
| Outcome: | The proposed approach preserves nearly 99% of visual performance and improves textual task results while reducing training time. |
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 . |
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)
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Jiaming Li, Yukun Chen, Ziqiang Liu, Minghuan Tan, Lei Zhang, Yunshui Li, Run Luo, Longze Chen, Jing Luo, Ahmadreza Argha, Hamid Alinejad-Rokny, Wei Zhou, Min Yang
| Challenge: | Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience. |
| Approach: | They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story. |
| Outcome: | The proposed approach outperforms existing methods in 84.33% of the trials. |
Exploring Generative Process Reward Modeling for Semi-Structured Data: A Case Study of Table Question Answering (2026.eacl-short)
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| Challenge: | Recent advances in process reward models (PRMs) have demonstrated remarkable improvements in the reasoning capabilities of large language models (LLMs). |
| Approach: | They evaluate state-of-the-art generative PRMs on table question answering from answer and step perspectives and compare their results to previous studies. |
| Outcome: | The proposed model can aid solution selection but struggle to generalize to out-of-domain data. |
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)
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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu
| Challenge: | Existing sparsification methods like pruning can lose model knowledge through parameter removal. |
| Approach: | They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. |
| Outcome: | The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints. |
ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks (2026.acl-long)
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Peizhuo Lv, Ruihua Zhou, Yunpeng Li, Ruigang Liang, Xingshuo Han, XiaoFeng Wang, Wei Dong, Yuling Liu
| Challenge: | Existing reasoning-enhanced large language models fail to provide reliable attribution of reasoning behavior once it is transferred through knowledge distillation. |
| Approach: | They propose to embed a reasoning-length gap in a model by querying a target domain and training a local student to imitate its outputs. |
| Outcome: | et al. show that ReasMark outperforms baselines while preserving task utility. |
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential (2024.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models. |
| Approach: | They propose a "generate-then-read" pipeline to replace retrieval stage with generation from the LLM itself. |
| Outcome: | The proposed framework outperforms single models in the base and chat versions and addresses safety and helpfulness post-adaptation challenges. |
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)
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| Challenge: | SQA is an emerging application of NLP in the medical, geography, and legal domains. |
| Approach: | They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level. |
| Outcome: | The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level. |
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)
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| Challenge: | Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. |
| Approach: | They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs. |
| Outcome: | The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
Joint Optimization of Training Data and Policy in RLHF (2026.findings-acl)
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Zhuohao Yu, Jiali Zeng, Weizheng Gu, Mengyuan Sun, Yidong Wang, Fandong Meng, Jie Zhou, Shikun Zhang, Wei Ye
| Challenge: | JODP optimizes policies on fixed training inputs, limiting the diversity of learning signals. |
| Approach: | They propose a framework where policy generates improved variants of training problems to enhance its own learning. |
| Outcome: | The proposed framework improves on safety alignment tasks by allowing 4B models to reach 8B model performance with less than 1% additional computational overhead. |
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings (2024.lrec-main)
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| Challenge: | Currently, prompt-based models are gaining popularity due to their easier adaptability in low-resource settings. |
| Approach: | They analyze attribution scores extracted from prompt-based models w.r.t. plausibility and faithfulness and compare them with attribution score extracted from fine-tuned models and large language models. |
| Outcome: | The proposed model outperforms attention and Integrated Gradients in plausibility and faithfulness, while fine-tuning models are harder to explain in low-resource settings. |
Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)
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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin
| Challenge: | Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art. |
| Approach: | They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data. |
| Outcome: | The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art. |
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion Cause Pair Extraction (2022.findings-acl)
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| Challenge: | Existing methods to extract emotions and causes as pairs neglect effective semantic connections between distant clauses, leading to poor generalization ability towards position-insensitive data. |
| Approach: | They propose a novel multi-granularity semantic-aware Graph model to integrate fine-grained and coarse-grain semantic features together without regard to distance limitation. |
| Outcome: | The proposed model outperforms existing models significantly in position-insensitive data. |
Phased Instruction Fine-Tuning for Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods to enhance pre-trained language models' ability to follow instructions are limited due to the simultaneous handling of varying instruction complexities. |
| Approach: | They propose a phased instruction fine-tuning method that posits that the transition of a pre-trained language model from simple next-word prediction to sophisticated instruction following is a gradual learning process. |
| Outcome: | The proposed method surpasses the one-off instruction fine-tuning method in win rate and validates the hypothesis of progressive alignment. |
Scheduled DropHead: A Regularization Method for Transformer Models (2020.findings-emnlp)
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| Challenge: | DropHead is a structured dropout method for regularizing multi-head attention . DropHed drops entire attention heads during training to prevent overfitting . |
| Approach: | They propose a structured dropout method specifically designed for regularizing multi-head attention mechanism . DropHead drops entire attention heads during training to prevent overfitting . |
| Outcome: | The proposed method can improve transformer models by 0.9 BLEU score on translation task and around 1.0 accuracy for various text classification tasks. |
Making Harmful Behaviors Unlearnable for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are often customized by fine-tuning for the requirements of different domains. |
| Approach: | They propose a controllable training framework to make undesired behaviors unlearnable during the fine-tuning process. |
| Outcome: | The proposed framework makes undesired behaviors unlearnable during the fine-tuning process while preserving the ability to learn other information. |
GA-S3: Comprehensive Social Network Simulation with Group Agents (2025.findings-acl)
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| Challenge: | Existing social network simulations focus on discrete events or system dynamics instead of elucidating underlying mechanisms or causal relationships. |
| Approach: | They propose a Social network simulation system that leverages newly designed Group Agents to make intelligent decisions regarding various online events. |
| Outcome: | The proposed system can make intelligent decisions regarding online events at a manageable cost. |
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. |
| Approach: | They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training . |
| Outcome: | Experiments show that models with proposed model can improve on downstream benchmarks. |
EventWiki: A Knowledge Base of Major Events (L18-1)
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| Challenge: | Existing knowledge bases focus on static entities such as people, locations and organizations. |
| Approach: | They propose a new knowledge base resource called EventWiki which concentrates on major events . they show that EventWiki is a very useful resource for information extraction regarding events in NLP . |
| Outcome: | The proposed resource is the first knowledge base resource of major events. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)
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| Challenge: | Recent years have witnessed a growing interest in the development of explainable recommendation models. |
| Approach: | They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models. |
Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots (2025.findings-emnlp)
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| Challenge: | Current studies rely on simplistic user and network modeling and neglect dynamic behavior of bots. |
| Approach: | They propose a multi-agent-based framework for disinformation dissemination . it incorporates both malicious and legitimate bots and allows quantitative evaluation of correction strategies. |
| Outcome: | The proposed framework incorporates both malicious and legitimate bots and their controlled dynamic participation allows for quantitative analysis of correction strategies. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
LLMs as Collaborator: Demands-Guided Collaborative Retrieval-Augmented Generation for Commonsense Knowledge-Grounded Open-Domain Dialogue Systems (2024.findings-emnlp)
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| Challenge: | Existing methods to capture the unique knowledge demands of LLMs are unsatisfactory because of their overestimation and lack of knowledge. |
| Approach: | They propose a novel approach to capture the unique knowledge demands for each dialogue context using CoT and RAG methods. |
| Outcome: | The proposed model can capture the unique knowledge demands for each dialogue context and bring higher-quality responses. |
Regularized Contrastive Decoding with Hard Negative Samples for LLM Hallucination Mitigation (2025.findings-emnlp)
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| Challenge: | Large language models are prone to generate hallucinations, which can undermine their reliability in high-stakes applications. |
| Approach: | They propose a method to capture hallucination signals for mitigating hallucis in large language models by regularizing the model's internal signals to a weaker model . |
| Outcome: | The proposed method achieves better hallucination mitigation performance on four benchmarks. |
Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems (2024.naacl-long)
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| Challenge: | Existing studies on dialogue response selection focus on post-training and fine-tuning for cross-encoders. |
| Approach: | They propose a post-training technique tailored for dense encoders in dialogue response selection . they propose 'Dialogue Contextual Masking Auto-Encoder' which compresses dialogue semantics into dense vectors . |
| Outcome: | The proposed technique achieves state-of-the-art on two commonly evaluated benchmarks. |
Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition (D18-1)
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| Challenge: | a novel information network decipherment paradigm is proposed for fine-grained coordinated cross-lingual text stream alignment. |
| Approach: | They propose to use Burst Information Networks as media to represent text streams . they propose a simple yet effective information network decipherment algorithm with diverse clues . |
| Outcome: | The proposed approach outperforms existing approaches on bilingual lexicon extraction from coordinated text streams and can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining. |
Aligning Large Language Models for Controllable Recommendations (2024.acl-long)
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| Challenge: | Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template. |
| Approach: | They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions. |
| Outcome: | The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy. |
Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)
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| Challenge: | Current error-handling works are performed in a passive manner, with explicit error- handling instructions. |
| Approach: | They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research. |
| Outcome: | The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances. |
A Discrete CVAE for Response Generation on Short-Text Conversation (D19-1)
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| Challenge: | Neural conversation models are easy to generate bland and generic responses . however, their improvement of generating high-quality responses is still unsatisfactory . |
| Approach: | They propose to use a discrete latent variable with an explicit semantic meaning to improve the conditional variational autoencoder on short-text conversation. |
| Outcome: | The proposed model outperforms various kinds of generation models under automatic and human evaluations and generates more diverse and informative responses. |
Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets (2025.emnlp-main)
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| Challenge: | Existing mitigation strategies for Text-to-Speech systems require excessive training resources or inference latency. |
| Approach: | They propose a GFlOwNet-guided distribution AlignmenT framework that mitigates hallucinations without relying on massive resources or inference latency. |
| Outcome: | The proposed framework reduces over 50% character error rates and lowers uncertainty by up to 58% on challenging test cases. |
Extending Context Window of Large Language Models via Semantic Compression (2024.findings-acl)
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| Challenge: | Existing models rely on a quadratic computation to generate long texts . current models impose limitations on the length of text inputs . |
| Approach: | They propose a semantic compression method that extends the context window of large language models . the method reduces the semantic redundancy of long inputs before passing them to the LLMs . |
| Outcome: | The proposed method extends the context window of large language models across tasks . it exhibits consistent fluency in text generation while reducing associated computational overhead. |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |
Neural Document Summarization by Jointly Learning to Score and Select Sentences (P18-1)
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| Challenge: | Sentence scoring and sentence selection are two main steps in extractive document summarization systems. |
| Approach: | They propose an end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. |
| Outcome: | The proposed framework outperforms the state-of-the-art summarization models on the CNN/Daily Mail dataset. |
Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory (2025.naacl-long)
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| Challenge: | Existing privacy studies focus on sub-fields, but they focus on a few sub-domains. |
| Approach: | They propose to use the Health Insurance Portability and Accountability Act of 1996 as an example to develop a checklist that covers social identities, private attributes, and existing privacy regulations. |
| Outcome: | The proposed checklist covers social identities, private attributes, and existing privacy regulations. |
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)
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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei
| Challenge: | Existing work shows that pre-trained models can improve in various natural language processing tasks. |
| Approach: | They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data. |
| Outcome: | The proposed framework is superior to existing models on speech-to-text processing tasks. |
UrbanGeoEval: A City-Scale Benchmark for Evaluating Large Language Models in Geospatial Reasoning (2026.acl-long)
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| Challenge: | Extensive experiments on 18 widely used LLMs uncover critical insights: (1) models exhibit severe geographic biases and resolution gaps; (2) failures in complex multi-hop tasks stem from brittle foundational spatial skills rather than high-level logic deficits. |
| Approach: | They propose a dual-module framework that disentangles factual recall and spatial logic from the model's real capabilities in urban environments. |
| Outcome: | Extensive tests on 18 widely used LLMs reveal that models exhibit severe geographic biases and resolution gaps, and failures in complex multi-hop tasks often stem from brittle foundational spatial skills rather than high-level logic deficits. |
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025.acl-long)
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| Challenge: | MM-Verifier and MM Reasoner are a powerful multimodal reasoning model . large language models (LLMs) have demonstrated exceptional performance across tasks spanning myriad domains. |
| Approach: | They propose a method which combines tree search and verification to generate high-quality chain-of-thought data. |
| Outcome: | The proposed method outperforms all larger models on the MathCheck, MathVista, and MathVerse benchmarks. |
R-CHAR: A Metacognition-Driven Framework for Role-Playing in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing role-playing structures lack cognitive consistency in complex scenarios . Existing models excel in math and coding tasks but lack coherent reasoning . |
| Approach: | They propose a metacognition-driven framework that enhances role-playing performance . experimental results show performance improvements across varying scenario complexities . |
| Outcome: | The proposed framework outperforms existing models in social intelligence tasks and shows strength in long-context comprehension and group-level social interactions. |
Trait Activation in Silicon: A Situation-Aware Framework for Psychologically Grounded Role-Playing (2026.acl-long)
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| Challenge: | Role-playing agents lack a deep understanding of complex human psychological mechanisms. |
| Approach: | They propose a situation-aware framework that decouples personality traits into bidirectional LoRA adapters. |
| Outcome: | Empirical results show that PD-LLM achieves superior performance in both static fidelity and dynamic adaptability. |
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)
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| Challenge: | Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive. |
| Approach: | They propose a framework which iteratively balances training data within a fixed computational budget and enables the transfer of knowledge from expensive teacher LLMs to smaller student models. |
| Outcome: | The proposed framework achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models. |
“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition (2020.acl-main)
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| Challenge: | Identifying and modeling puns is challenging as they involve implicit semantic or phonological tricks. |
| Approach: | They propose a method to detect puns in a sentence and then locate them in it . they propose to capture phonetic associations between the context and phonetic symbols . |
| Outcome: | The proposed method outperforms state-of-the-art methods in pun detection and location tasks. |
Multi-Grained Knowledge Distillation for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Pre-trained big models have delivered top performance in Seq2seq modeling, but their deployments in real-world applications are often hindered by excessive computations and memory demands. |
| Approach: | They propose a distillation scheme to efficiently transfer knowledge from big models to their cheaper counterparts. |
| Outcome: | The proposed scheme maximizes the assimilation of knowledge from the teacher model to the student model. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)
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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao
| Challenge: | Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions. |
| Outcome: | The proposed framework improves generalization and realism of large language models under complex and irregular table conditions. |
Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023.emnlp-industry)
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| Challenge: | Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging contextual information from previous user-agent conversations to improve comprehension of current user intent. |
| Approach: | They propose a framework to enhance the CQR model's capability in generating user preference-aligned rewrites. |
| Outcome: | The proposed framework improves the CQR model's ability to generate user preference-aligned rewrites. |
False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize (2026.findings-acl)
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| Challenge: | Recent work has leveraged probing-based approaches to study the separability of malicious and benign inputs in Large Language Models’ internal representations. |
| Approach: | They propose to use probing-based methods to study separability of malicious and benign inputs in LLMs' internal representations to detect harmful and benign content. |
| Outcome: | The proposed methods show that they learn superficial patterns rather than semantic harmfulness. |
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)
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| Challenge: | Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments. |
| Approach: | They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts. |
| Outcome: | The proposed method achieves superior performance on a large dataset for propaganda detection. |
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)
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Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu
| Challenge: | Existing datasets address understanding and generation in isolation, limiting the performance of unified vision large language models. |
| Approach: | They propose a dataset that facilitates mutual enhancement between multimodal understanding and generation. |
| Outcome: | The proposed framework integrates diverse visual and textual inputs and outputs, enabling comprehensive cross-modal reasoning and precise text-to-image alignment. |
Bitnet.cpp: Efficient Edge Inference for Ternary LLMs (2025.acl-long)
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Jinheng Wang, Hansong Zhou, Ting Song, Shijie Cao, Yan Xia, Ting Cao, Jianyu Wei, Shuming Ma, Hongyu Wang, Furu Wei
| Challenge: | 1-bit large language models have spurred interest in ternary LLMs, but efficient edge inference is still scarce. |
| Approach: | They propose an inference system optimized for 1-bit large language models . they propose a new library that facilitates sub-2-bits-per-weight inference . |
| Outcome: | The proposed inference system achieves 6.25x speed increase over full-precision baselines and 2.32x over low-bit baselines. |
Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)
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| Challenge: | Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error. |
| Approach: | They propose to use flowcharts to evaluate existing LLMs' code generation capabilities. |
| Outcome: | The proposed benchmarks show that the supervised fine-tuning technique contributes greatly to the models’ performance. |
STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)
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Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu
| Challenge: | Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence. |
| Approach: | They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty. |
| Outcome: | Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect. |
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)
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| Challenge: | Entity linking is a task of assigning entity mentions to referent entities in a knowledge base. |
| Approach: | They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information. |
| Outcome: | The proposed model achieves state-of-the-art in the zero-shot entity linking task . |
To be Closer: Learning to Link up Aspects with Opinions (2021.emnlp-main)
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| Challenge: | Dependency parsers are not designed for capturing interaction between opinion words and aspect words. |
| Approach: | They propose to learn an aspect-centric tree structure to shorten distance between aspects and opinion words. |
| Outcome: | The proposed model outperforms baselines on five aspect-based sentiment datasets. |
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification (2021.acl-long)
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| Challenge: | Existing studies for multi-label text classification do not explore label-specific semantic components from documents. |
| Approach: | They propose a label-specific dual graph neural network that incorporates category information to learn label-related components from documents. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets and achieves better performance with respect to tail labels. |
FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering (2024.naacl-long)
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| Challenge: | Existing benchmarks for Table Question Answering lack robustness . current systems generate inconsistent responses by performing coarse-grained changes in tables and questions. |
| Approach: | They formalize three major desiderata for a fine-grained evaluation of robustness of TQA systems . they aim to evaluate TQA responses on the content of relevant cells rather than on biases . |
| Outcome: | The proposed benchmarks show that state-of-the-art TQA systems fail in three aspects . the benchmarks are released publicly and are expected to improve over time . |
SADA: Bridging In-Context Learning and Fine-Tuning via State-Aligned Distillation Adapters (2026.acl-long)
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| Challenge: | Prompt-based in-context learning and parameter fine-tuning are dominant paradigms for incorporating external information into large language models, but they incur high inference costs or require expensive retraining. |
| Approach: | They propose to convert prompts into temporary adapter weights to bridge this gap by converting prompts to temporary adapters. |
| Outcome: | The proposed model outperforms baselines on long-context language modeling and downstream NLU and summarization benchmarks while significantly reducing memory footprint and latency. |
AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have improved text generation and reasoning. |
| Approach: | They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility. |
| Outcome: | The proposed framework embeds multi-bit provenance into planning decisions while preserving utility. |
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks (2025.findings-acl)
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| Challenge: | Existing models with limited performance and limited training can be difficult to use in large-scale applications. |
| Approach: | They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks. |
| Outcome: | The proposed method outperforms 13 baseline models and reduces costs by 17.20%. |
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning (D18-1)
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| Challenge: | Existing systems for automatic essay scoring are trained to predict the score of each essay at a time without considering rating schema. |
| Approach: | They propose a reinforcement learning framework that incorporates quadratic weighted kappa as guidance to optimize the scoring system. |
| Outcome: | Experiments on benchmark datasets show the proposed framework is effective. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
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| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)
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| Challenge: | Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. |
| Approach: | They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification. |
| Outcome: | The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification. |
DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing (2026.acl-long)
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| Challenge: | Existing methods for enhancing large language models (LLMs) lack explicit mechanisms for guiding diverse exploration and instead prioritize efficiency and performance over diversity. |
| Approach: | They propose a reinforcement learning-based framework that decomposes the generation process into explicitly planned intermediate steps and introduces divergence at the planning phase based on diversity variation. |
| Outcome: | The proposed method significantly outperforms existing baselines on creative writing benchmarks on a semi-structured long chain-of-thought (CoT) it introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories. |
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. |
Fluency Boost Learning and Inference for Neural Grammatical Error Correction (P18-1)
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| Challenge: | Seq2seq models for grammatical error correction (GEC) have two limitations: (1) a seq2q model may not be well generalized with only limited error-corrected data; (2) a model may fail to completely correct a sentence with multiple errors through normal seq1sequeq inference. |
| Approach: | They propose a fluency boost learning and inference mechanism to improve the performance of seq2seq models for grammatical error correction (GEC) by generating fluency-boost sentence pairs during training. |
| Outcome: | Experiments show that the proposed model improves on both CoNLL-2014 and JFLEG benchmark datasets. |
Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation (2026.findings-acl)
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| Challenge: | Existing RAG systems require uploading local documents to the cloud, resulting in inference latency and poor generalization on out-of-distribution (OOD) inputs. |
| Approach: | They propose a generalizable knowledge-distilled parametric RAG model aligned with standard RAG in document structure and parameter activation. |
| Outcome: | The proposed model outperforms baselines in accuracy and generalizes well on out-of-distribution (OOD) data. |
BERT-based Lexical Substitution (P19-1)
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| Challenge: | Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence. |
| Approach: | They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources. |
| Outcome: | The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks. |
Teaching-Assistant-in-the-Loop: Improving Knowledge Distillation from Imperfect Teacher Models in Low-Budget Scenarios (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated state-of-the-art (SOTA) performance across a wide spectrum of tasks. |
| Approach: | They propose a framework that leverages three signal types to improve efficiency within resource-constrained, imperfect teacher scenarios. |
| Outcome: | The proposed framework improves on four complex reasoning tasks by 20.79% compared to fine-tuning without any signals across datasets. |
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models (2025.emnlp-main)
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| Challenge: | Recent research has focused on smaller, task-specific models enhanced by distilling knowledge from LLMs, but the diversity and quality of negative knowledge remains understudied. |
| Approach: | They propose a quality-guided contrastive rationale distillation framework that aims to enhance reasoning capabilities through contrastive knowledge learning. |
| Outcome: | The proposed method consistently outperforms existing distillation techniques yielding higher-quality rationales. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2021.naacl-main)
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Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou
| Challenge: | Existing methods for learning cross-lingual representations are lacking in the field of NLP. |
| Approach: | They propose a framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. |
| Outcome: | The proposed approach improves cross-lingual transferability on benchmarks. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |
Impartial Multi-task Representation Learning via Variance-invariant Probabilistic Decoding (2025.acl-long)
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| Challenge: | Existing methods focus on balancing loss or gradients but fail to address this issue due to the representation discrepancy in latent space. |
| Approach: | They propose a framework that harmonizes representation spaces across tasks to ensure impartial learning by harmonizing representation spaces. |
| Outcome: | The proposed framework outperforms 12 representative methods under the same multi-task settings, especially in heterogeneous task combinations and data-constrained scenarios. |
Imitation Learning for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Existing non-autoregressive translation models lack parallel decoding, which is a bottleneck for NMT decoding. |
| Approach: | They propose a framework for non-autoregressive machine translation that emulates the autoregressive model by sampling sentence length in parallel. |
| Outcome: | The proposed model achieves 31.85 BLEU on WMT16 RoEn and 30.68 BLUE on IWSLT16 EnDe on the IWSLD16, WMT14 and WMT15 datasets. |
Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings (2025.findings-acl)
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| Challenge: | Existing studies on white-box attacks focus on black-box LLMs, leaving black- box scenarios underexplored. |
| Approach: | They propose an automated algorithm designed for black-box LLMs that constructs the DoS Attack Tree and expands the node coverage to achieve effectiveness under black- box conditions. |
| Outcome: | The proposed algorithm can be used to build a DoS Attack Tree and expand the node coverage to achieve effectiveness under black-box conditions. |