Papers by Li Gu

Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning (2025.emnlp-main)

Challenge:	Test-time computing approaches that leverage additional computational resources during inference have been proven effective in enhancing large language model performance.
Approach:	They propose a linearly scaling approach that leverages local consistency of neighboring unlabeled data to improve test-time predictions.
Outcome:	The proposed approach outperforms baseline methods such as prompting and self-consistency across eight datasets and performs robustly across embedding models.

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Haijiang Liu, Qiyuan Li, Chao Gao, Yong Cao, Xiangyu Xu, Xun Wu, Daniel Hershcovich, Jinguang Gu

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

Challenge:	Introducing MARK, a framework for cultural value survey simulation . based on type dynamics theory, it improves accuracy and interpretation of models .
Approach:	They propose a framework that integrates psychological theory into cultural value survey simulations.
Outcome:	The proposed framework outperforms baseline models on the World Values Survey by 10% accuracy and reduces divergence between model predictions and human preferences.

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA (2025.findings-acl)

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Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li

Question Answering

Challenge:	Current long-context large language models lack citations to support their responses, making verification difficult due to potential hallucinations.
Approach:	They propose to use off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations and leverage this pipeline to construct a large-scale SFT dataset for LQAC.
Outcome:	The proposed pipeline can generate responses with fine-grained citations on the fly, surpassing existing models including GPT-4o.

MolRAG: Unlocking the Power of Large Language Models for Molecular Property Prediction (2025.acl-long)

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Ziting Xian, Jiawei Gu, Lingbo Li, Shangsong Liang

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)

Challenge:	Recent LLMs exhibit limited effectiveness on molecular property prediction task due to semantic gap between representations and natural language and lack of domain-specific knowledge.
Approach:	They propose a framework that integrates Chain-of-Thought reasoning for molecular property prediction.
Outcome:	The proposed framework outperforms pre-trained LLMs on four datasets and matches supervised methods.

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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)

Challenge:	Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly.
Approach:	They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show .
Outcome:	The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models.

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Xin Lv, Yuxian Gu, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu

BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression (2025.findings-naacl)

Challenge:	Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations .
Approach:	They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios.
Outcome:	The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL.

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Yuankai Li, Jia-Chen Gu, Di Wu, Kai-Wei Chang, Nanyun Peng

Information Retrieval

Challenge:	Existing approaches to augment language models with external knowledge but they are limited by static nature of pre-training data.
Approach:	They propose a lightweight approach that compresses retrieved documents into highly dense textual summaries to integrate into in-context RAG.
Outcome:	The proposed approach reduces latency and costs while achieving high performance in open-domain questions.

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs (2025.acl-long)

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Yuchen Fu, Zifeng Cheng, Zhiwei Jiang, Zhonghui Wang, Yafeng Yin, Zhengliang Li, Qing Gu

IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)

Challenge:	Recent studies have focused on prompt engineering to extract sentence embeddings from large language models (LLMs) but these models are mostly decoder-only and the earlier tokens in the sentence cannot attend to the latter, resulting in biased encoding of sentence information and cascading effects on the final decoded token.
Approach:	They propose a plug-and-play and training-free technique that prepends each layer’s decoded sentence embedding to the beginning of the sentence in the next layer’ s input.
Outcome:	The proposed technique can significantly improve the performance of existing prompt-based sentence embedding methods across different LLMs while incurring negligible additional inference cost.

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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

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

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.

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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang

DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

Challenge:	Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach:	They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome:	The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Challenge:	commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach:	They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome:	The proposed approach outperforms human experts in medical examinations on diverse datasets.

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization (2025.acl-long)

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

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Zhouhong Gu, Xingzhou Chen, Xiaoran Shi, Tao Wang, Suhang Zheng, Tianyu Li, Hongwei Feng, Yanghua Xiao

A Survey on LLM-based Conversational User Simulation (2026.eacl-long)

Challenge:	Existing methods for achieving this require a limited understanding of constraints and can be hallucinating or brittle.
Approach:	They propose a framework that combines adversarial training dynamics with an encoder-only reward model to progressively learn and adapt to increasingly complex constraints.
Outcome:	Extensive experiments show that GAPO significantly outperforms existing methods like PPO, DPO, and KTO in fine-grained constraints.

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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi

Dialogue Systems & Conversational Agents

Challenge:	Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation.
Approach:	They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments .
Outcome:	The proposed model enables high-fidelity generation of synthetic user conversation.

Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation (2020.acl-main)

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Arya D. McCarthy, Xian Li, Jiatao Gu, Ning Dong

Machine Translation

Challenge:	Existing variational inference models ignore their latent variables, a phenomenon called posterior collapse.
Approach:	They propose a new loss function for conditional variational autoencoders that counteracts posterior collapse by using a modified evidence lower bound objective and a factorized decoder.
Outcome:	The proposed model yields improved translation quality compared to existing models on WMT RoEn and DeEn.

Adaptive Backtracking for Privacy Protection in Large Language Models (2026.findings-acl)

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Zhihao Yao, Yuxuan Gu, Xiachong Feng, Weitao Ma, Bo Li, Xiaocheng Feng, Bing Qin

LLM Jailbreak Detection for (Almost) Free! (2025.findings-emnlp)

Challenge:	Existing privacy protection methods are prone to privacy leakage, but they are not effective in ensuring the privacy of users.
Approach:	They propose to capture latent leakage tendency of large language models during generation process and to construct a new benchmark for personal information.
Outcome:	The proposed method improves privacy by up to 14% over strong baselines against adversarial attacks, avoiding the degradation of response utility.

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Guorui Chen, Yifan Xia, Xiaojun Jia, Zhijiang Li, Philip Torr, Jindong Gu

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook (2026.acl-long)

Challenge:	Existing methods for detecting jailbreak prompts entail significant computational costs .
Approach:	They propose a free jailbreak detection method which scales logits by temperature to detect jailbreak prompts .
Outcome:	The proposed method detects jailbreak prompts with no additional computational costs.

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Hao Gu, Lujun Li, Hao Wang, Lei Wang, Zheyu Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Sirui Han, Yike Guo

Text Clustering

Challenge:	Recent sparsity-aware binarization approaches can achieve sub-1-bit compression, but they face performance degradation, mask-management overhead, and limited hardware compatibility.
Approach:	They propose a binary quantization framework that leverages binary pattern clustering and weight transformation to overcome performance degradation and mask-management overhead.
Outcome:	The proposed framework achieves state-of-the-art compression (1.11–0.7 bits) it maintains high performance with only a 3.1% accuracy drop in zero-shot benchmarks while delivering a 1.6 speedup over FP16.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience (2025.findings-acl)

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.

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Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li

The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations? (2024.findings-acl)

Challenge:	Large language models struggle to infer implicit relationships embedded in tabular formats . authors introduce a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL).
Approach:	They propose a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning to simulate human-like knowledge transfer.
Outcome:	Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance . it achieves gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks .

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Alex Gu, Wen-Ding Li, Naman Jain, Theo Olausson, Celine Lee, Koushik Sen, Armando Solar-Lezama

Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)

Challenge:	Language models are more proficient at code generation, but they still generate incorrect programs.
Approach:	They define a group of models that have a high log-probability and weak correctness checks.
Outcome:	The proposed model samples fail to understand counterfeits through three clear failure modes . counterfeits are confusing to the model as they are to other models, the authors say .

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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang

ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning (2026.acl-long)

Challenge:	Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability.
Approach:	They propose an off-policy influence estimation method that approximates data influence using offline trajectories.
Outcome:	The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency.

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Xuan Xiong, Huan Liu, Li Gu, Zhixiang Chi, Yue Qiu, Yuanhao YU, Yang Wang

AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation (2024.emnlp-main)

Challenge:	Existing methods to shorten CoTs use length penalties or global entropy reduction . Existing approaches to CoT reasoning have significant practical drawbacks .
Approach:	They propose a method that shortens CoTs by length penalties or global entropy reduction . they integrate ETR into Group Relative Policy Optimization and evaluate it .
Outcome:	The proposed objective improves accuracy–efficiency trade-off by +9.9% while reducing CoT length by 67% across four benchmarks.

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Wenhao Huang, Zhouhong Gu, Chenghao Peng, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Liqian Wen, Zulong Chen

Challenge:	Existing methods for web scraping suffer from limited adaptability and scalability when faced with a new website.
Approach:	They propose a framework that generates web scrapers with large language models and a new executability metric to measure the performance of web scraper generation tasks.
Outcome:	The proposed framework can handle diverse web environments more efficiently.

Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)

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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

Challenge:	In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of dynamic benchmarks .
Approach:	They propose a series of optimal design principles for dynamic benchmarking and analyze the limitations of existing static benchmarks.
Outcome:	The proposed benchmarks highlight a critical gap in the evaluation of LLMs.

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer

Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation (2025.findings-emnlp)

Challenge:	Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation.
Approach:	They propose a sequence-to-sequence denoising auto-encoder pre-trained on monolingual corpora . they show that it produces significant performance gains across MT tasks .
Outcome:	The proposed model can achieve significant performance gains across a wide variety of MT tasks.

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Jihao Gu, Yingyao Wang, Meng Cao, Pi Bu, Jun Song, Bo Zheng, Yancheng He, Shilong Li

Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)

Challenge:	Existing methods for predicting hallucinations suffer from two drawbacks: Lack of scalable token-level rewards and Neglect of visual-anchored tokens.
Approach:	They propose a Token Preference Optimization model with self-calibrated rewards . they propose based on visual-anchored tokens and visual-aware training objective .
Outcome:	The proposed model improves hallucination performance by focusing on visual-anchored tokens without fine-grained annotations.

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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao

Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)

Challenge:	Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding.
Approach:	They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss .
Outcome:	The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss.

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Zhuoyang Wu, Xinze Li, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Minghe Yu, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)

Challenge:	Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models.
Approach:	They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines.

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Yifan Li, YuKai Gu, Yingqian Min, Zikang Liu, Yifan Du, Kun Zhou, Min Yang, Xin Zhao, Minghui Qiu

On-policy Reinforcement Fine-tuning with Offline reward for Multi-step Embodied Planning (2026.acl-long)

Challenge:	Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking.
Approach:	They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result.
Outcome:	The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking.

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Di Wu, Jiaxin Fan, Chloe Gu, Guanbo Wang, Wei Yin, Wenhao Li, Bo Jin

See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)

Challenge:	Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and verbal goals.
Approach:	They propose an On-policy Reinforcement fine-tuning framework with offline rewards for Embodied Task Planning that preserves generalization benefits of RFT while addressing costly interaction and sparse rewards.
Outcome:	The proposed framework outperforms closed-source and online-RL methods on EmbodiedBench, a recent benchmark for interactive embodied tasks.

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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng

Domain-aware and Co-adaptive Feature Transformation for Domain Adaption Few-shot Relation Extraction (2024.lrec-main)

Challenge:	Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence.
Approach:	They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction.
Outcome:	The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge.

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Yijun Liu, Feifei Dai, Xiaoyan Gu, Minghui Zhai, Bo Li, Meiou Zhang

Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs (2026.acl-long)

Challenge:	Existing approaches to relation extraction focus on the source domain, which makes it difficult to accurately transfer useful knowledge to the target domain.
Approach:	They propose a domain-aware and co-adaptive feature transformation approach to address these issues by leveraging the target domain distribution features to guide the domain-based feature transformations.
Outcome:	The proposed method outperforms existing models and achieves state-of-the-art performance on a benchmark dataset.

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Binxing Xu, Hao Gu, Lujun Li, Hao Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Xintong Yang, Chao Li, Sirui Han, Yike Guo

SURE: Safety Understanding and Reasoning Enhancement for Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Existing approaches to training LLMs at ultra-low precisions suffer from convergence instability and substantial training costs.
Approach:	They propose a progressive QAT framework with outlier channel splitting to address these issues . they use nested structure of integer quantization grids to enable a "train once, deploy any precision" paradigm .
Outcome:	The proposed framework outperforms baselines on both Llama2/3 and W2A16, with an 11 speedup over BF16.

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Yuxin Gou, Xiaoning Dong, Qin Li, Shishen Gu, Richang Hong, Wenbo Hu

Multilingual Translation from Denoising Pre-Training (2021.findings-acl)

Challenge:	Existing multimodal large language models incorporate visual and textual information, but introduces new and complex safety risks.
Approach:	They propose a safety reasoning framework that integrates visual modalities into multimodal models to help them resist jailbreak attacks.
Outcome:	The proposed framework improves model safety while avoiding over-defense . it is based on a large-scale safety reasoning dataset .

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Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)

Challenge:	Recent work shows potential of training one model for multilingual machine translation . but little has been explored on the potential to combine denoising pretraining with multilingual translation in a single model.
Approach:	They propose to combine denoising pretraining with multilingual machine translation in a single model.
Outcome:	The proposed model improves over models trained from scratch and bilingually for translation into English.

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Qianren Mao, Shaobo Zhao, Jiarui Li, Xiaolei Gu, Shizhu He, Bo Li, Jianxin Li

PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)

Challenge:	Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives.
Approach:	They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features .
Outcome:	The proposed model outperforms heavy BERT- or RoBERTa-based sentence ranking methods by providing summary-worthy representations.

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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu

Challenge:	PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints.
Approach:	They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly.
Outcome:	The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives.

HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference (2026.acl-long)

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Bowen Zeng, Feiyang Ren, Jun Zhang, Xiaoling Gu, Ke Chen, Lidan Shou, Huan Li

Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios (2025.emnlp-main)

Challenge:	Multimodal Large Language Models (MLLMs) are hindered by the rapid growth of key–value (KV) caches.
Approach:	They propose a hybrid KV cache compression framework that reduces KV memory by 7.9 and speeds up decoding by 1.52.
Outcome:	Experiments on 11 multimodal benchmarks show that HYBRIDKV cuts KV cache memory by 7.9 and speeds up decoding by 1.52.

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Yunkai Dang, Mengxi Gao, Yibo Yan, Xin Zou, Yanggan Gu, Jungang Li, Jingyu Wang, Peijie Jiang, Aiwei Liu, Jia Liu, Xuming Hu

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)

Challenge:	Existing studies have focused mainly on visual–textual misalignment, leaving largely unexplored the MLLMs’ ability to preserve an original correct answer when confronted with misleading information.
Approach:	They propose a two-stage evaluation pipeline to quantify the response uncertainty phenomenon by eliciting each model’s original response on unperturbed inputs and injecting explicit (false-answer hints) and implicit (contextual contradictions) misleading instructions.
Outcome:	The proposed model overturns a correct answer in 65% of cases after receiving a single deceptive cue.

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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin

Probing the Safety Robustness of LLMs in Latent Space (2026.acl-long)

Challenge:	Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation .
Approach:	They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input.
Outcome:	The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions.

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Tianle Gu, Kexin Huang, Zongqi Wang, Yixu Wang, Jie Li, Xin Wang, Yang Yao, Yujiu Yang, Yan Teng, Yingchun Wang

Robustness in NLP

Challenge:	Despite substantial progress in safety alignment techniques, aligned large language models can still produce unsafe responses under minor internal perturbations.
Approach:	They introduce Activation Steering Attack (ASA) and leverage the Negative Log-Likelihood (NLL) as a diagnostic signal to probe the local sensitivity of safety behaviors in latent space.
Outcome:	The proposed method is model-agnostic and supervision-free, enabling a general and reproducible diagnostic metric for analyzing safety robustness.

Mitigating Spurious Correlations in Text Classification Using Latent Space Geometry (2026.acl-long)

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Jiasen Gao, Xiaoliang Chen, Duoqian Miao, Xu Gu, Xianyong Li, Yajun Du

Text Classification

Challenge:	Existing models rely on predictive shortcuts that hold in training data but break under distribution shifts, leading to large performance drops for minority groups.
Approach:	They propose a framework that transforms abstract biases into interpretable geometric anchors without auxiliary classifiers by manipulating latent space geometry.
Outcome:	The proposed framework outperforms state-of-the-art baselines and improves worst-group accuracy by over 20% on the CivilComments dataset.

Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)

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Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, Tianyi Zhou

Reimagining Safety Alignment with An Image (2025.emnlp-main)

Challenge:	Instruction tuning is critical to large language models but its success heavily relies on the training data quality.
Approach:	They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data.
Outcome:	The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data.

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Yifan Xia, Guorui Chen, Wenqian Yu, Zhijiang Li, Philip Torr, Jindong Gu

Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data (2023.findings-acl)

Challenge:	Existing approaches to large language models face inefficiency, fragility, or architectural constraints, resulting in inefficient performance and heightened over-refusal in cross-modal tasks.
Approach:	They propose an optimization-driven visual prompt framework that enhances security and reduces over-refusal at the same time.
Outcome:	The proposed framework enhances security and reduces over-refusal while maintaining robust safety while reducing unnecessary denials.

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Xinze Li, Zhenghao Liu, Chenyan Xiong, Shi Yu, Yu Gu, Zhiyuan Liu, Ge Yu

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)

Challenge:	Structure Aware Dense Retrieval (SANTA) model encodes user queries and structured data in one universal embedding space for retrieving structured data.
Approach:	They propose to use structured data and unstructured data to encode queries and structured data in one universal embedding space for retrieving structured data.
Outcome:	The proposed model achieves state-of-the-art on code search and product search and conducts convincing results in the zero-shot setting.

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Haidong Xin, Xinze Li, Zhenghao Liu, Yukun Yan, Shuo Wang, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

Anchored Cyclic Generation: A Novel Paradigm for Long-Sequence Symbolic Music Generation (2026.findings-acl)

Challenge:	Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows.
Approach:	They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% .
Outcome:	The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction.

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Boyu Cao, Lekai Qian, Dehan Li, Haoyu Gu, Mingda Xu, Qi Liu

Unveiling Inherent Visual Grounding in Multimodal LLMs for Text-Rich Images (2026.findings-acl)

Challenge:	Autoregressive sequence modeling has been successful in many domains, but maintaining long-term coherence and structural integrity remains a challenge.
Approach:	They propose an ACG paradigm that relies on anchor features from previously generated musical content to guide subsequent generation during the autoregressive process.
Outcome:	The proposed framework outperforms existing methods in symbolic music generation tasks.

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Shijie Zhou, Jihyung Kil, Ming Li, Jiuxiang Gu, Curtis Wigington, Rajiv Jain, Changyou Chen, Ruiyi Zhang

BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning (2026.findings-acl)

Challenge:	Existing multimodal large language model (MLLM) approaches struggle to align query tokens with visual–text patches, heavily relying on lengthy OCR inputs.
Approach:	They propose an OCR-free approach that leverages the MLLM's inherent multi-head attention for multi-patch grounding.
Outcome:	Empirical results show that the proposed approach outperforms existing approaches on challenging document grounding benchmarks.

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Jia-Chen Gu, Junyi Zhang, Di Wu, Yuankai Li, Kai-Wei Chang, Nanyun Peng

LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks (2026.acl-srw)

Challenge:	Experiments show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models.
Approach:	They propose a universal, lightweight compressor that distills relevant evidence from retrieved documents into a concise summary for seamless integration into in-context RAG.
Outcome:	Experiments on four open-domain multi-hop question-answering datasets show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models.

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Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yisi Sang, Yun Wang, Hansu Gu, Peng Zhang, Tun Lu, Toby Jia-Jun Li, Dakuo Wang

From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding (2025.emnlp-industry)

Challenge:	Deploying machine learning models in domain-specific scenarios is challenged by data drift and the scarcity of expert annotations.
Approach:	They propose a system that combines an LLM, an AL-assisted compact model and an automatic switch module to assist the active learning process.
Outcome:	The proposed system achieves 96–98% switch accuracy and outperforms both models used alone.

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Xiangfeng Wang, Xiao Li, Yadong Wei, null Songxueyu, Yang Song, null Xiaxiaoqiang, Fangrui Zeng, Zaiyi Chen, null Liuliu, Gu Xu, Tong Xu

Rubrik’s Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset (2025.acl-long)

Challenge:	Existing methods for video editing rely on textual cues from ASR transcripts and segment selection, often neglecting rich visual context.
Approach:	They propose a human-inspired automatic video editing framework that leverages multimodal narrative understanding to address these limitations.
Outcome:	The proposed framework outperforms existing baselines across general and advertisement-oriented editing tasks.

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Diana Galvan-Sosa, Gabrielle Gaudeau, Pride Kavumba, Yunmeng Li, Hongyi Gu, Zheng Yuan, Keisuke Sakaguchi, Paula Buttery

Explainability & Interpretability in NLP

Challenge:	Large-Language Models (LLMs) are increasingly being used in explanation generation tasks due to their unreliability.
Approach:	They propose a rubric and a dataset of 26k explanations written and quality-annotated using the rubric by humans and six open- and closed-source LLMs to test their proposed rubric.
Outcome:	The proposed rubric and CUBE dataset focuses on reasoning and language tasks and provides the necessary diversity to test it.

Enhanced Reasoning for Biomedical Document-Level Relation Extraction via a Novel Cascade Language Model Framework (2026.acl-long)

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Haohua Song, Wenhao Gu, Zhijing Li, null Yunwenyu, Tiantian Zhu, Xiao Yang, Zexuan Zhu

VISA: An Ambiguous Subtitles Dataset for Visual Scene-aware Machine Translation (2022.lrec-1)

Challenge:	Pre-trained language models (PLMs) are the leading paradigm in document-level relation extraction.
Approach:	They propose a cascade framework that leverages the complementary strengths of PLMs and LLMs through a detect-then-rethink paradigm.
Outcome:	The proposed framework improves on BioRED and CDR datasets and improves existing models.

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Yihang Li, Shuichiro Shimizu, Weiqi Gu, Chenhui Chu, Sadao Kurohashi

Unsupervised Text Style Transfer for Controllable Intensity (2026.findings-eacl)

Challenge:	Existing multimodal machine translation datasets contain images and video captions or general subtitles which rarely contain linguistic ambiguity.
Approach:	They propose a dataset that consists of Japanese-English parallel sentence pairs and corresponding video clips.
Outcome:	The proposed dataset is challenging for the latest MMT system and can facilitate MMT research.

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Shuhuan Gu, Wenbiao Tao, Xinchen Ma, Kangkang He, Ye Guo, Xiang Li, Yunshi Lan

Low-Resource NLP

Challenge:	Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs.
Approach:	They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels.
Outcome:	The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity.

TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain (2025.emnlp-main)

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Bohao Chu, Meijie Li, Sameh Frihat, Chengyu Gu, Georg Lodde, Elisabeth Livingstone, Norbert Fuhr

Summarization

Challenge:	Existing evidence-based summarization tasks require tracing source evidence to assess their accuracy.
Approach:	They propose a benchmark for traceable, aspect-based summarization that pairs summaries with sentence-level citations to enable users to trace back to the original context.
Outcome:	The proposed benchmark can be used to evaluate document summarization with LLMs and human evaluations.

Capturing Nuanced Preferences: Preference-Aligned Distillation for Small Language Models (2025.findings-acl)

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Yanggan Gu, Junzhuo Li, Sirui Huang, Xin Zou, Zhenghua Li, Xuming Hu

2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision (2025.findings-naacl)

Challenge:	Existing methods for aligning small language models with human values model preference knowledge from large language models (LLMs) however, this limitation hinders student SLMs from capturing nuanced preferences for multiple responses.
Approach:	They propose a framework which models teacher's preference knowledge as a probability distribution over all potential preferences, thereby providing more nuanced supervisory signals.
Outcome:	The proposed framework outperforms existing methods on four benchmark tasks and achieves 20% improvement on AlpacaEval 2 and Arena-Hard.

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Shilong Li, Yancheng He, Hui Huang, Xingyuan Bu, Jiaheng Liu, Hangyu Guo, Weixun Wang, Jihao Gu, Wenbo Su, Bo Zheng

Challenge:	Existing methods that optimize for scalar scores or ranking reward ignore multi-dimensional nature of human preferences.
Approach:	They propose to extend the preference of Direct Preference Optimization to two dimensions: segments and aspects.
Outcome:	The proposed framework decomposes the overall objective into multi-segment and multi-aspect objectives.

StructFact: Reasoning Factual Knowledge from Structured Data with Large Language Models (2025.findings-acl)

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Sirui Huang, Yanggan Gu, Zhonghao Li, Xuming Hu, Li Qing, Guandong Xu

Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)

Challenge:	Large language models (LLMs) have made significant strides in natural language processing by leveraging their ability to comprehend and reason with factual knowledge.
Approach:	They propose a benchmark to evaluate the ability of large language models to reason with structured data for knowledge-intensive tasks.
Outcome:	Extensive tests on 10 common LLMs show that they struggle with heterogeneity of structured data during reasoning.

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Zhen Li, Xiaohan Xu, Tao Shen, Can Xu, Jia-Chen Gu, Yuxuan Lai, Chongyang Tao, Shuai Ma

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

Challenge:	introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance.
Approach:	They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them.
Outcome:	The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods.

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration (2026.acl-short)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Hang Lv, Hongchao Gu, Ruiqing Yang, Liangyue Li, Zulong Chen, Defu Lian, Hao Wang, Enhong Chen

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning for LLMs (2026.findings-acl)

Challenge:	Existing methods for listwise reranking exhibit intrinsic position bias . existing methods are constrained by an inherent trade-off between efficiency and flexibility .
Approach:	They propose a training-free framework that mechanically decouples positional bias from ranking decisions.
Outcome:	a training-free framework decouples position bias from ranking decisions . evaluations show it outperforms training-based methods and outperformed expensive methods .

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Huanyu Liu, Jia Li, Yihong Dong, Chang Yu, Taozhi Chen, Lecheng Wang, Yongding Tao, Bin Gu, Ge Li

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization (2026.acl-long)

Challenge:	Existing approaches to reinforcement learning with verifiable reward (RLVR) are limited by difficulty or lack of exploration.
Approach:	They propose a self-evolving curriculum learning framework based on chain-of-thought reasoning optimization that constrains exploration space by self-generating and verifying CoT trajectories.
Outcome:	The proposed framework enables LLMs to solve previously unsolved problems without external supervision and is compatible with various RL fine-tuning methods.

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Shaohua Duan, Pengcheng Huang, Xinze Li, Zhenghao Liu, Xiaoyuan Yi, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun

Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations (2024.acl-long)

Challenge:	Recent studies have explored fine-tuning Large Language Models with synthetic data to enhance their long-context capabilities.
Approach:	They propose a framework that leverages a Multi-Armed Bandit rollout strategy to identify the most informative chunks from the given long context for sampling high-quality and diverse responses.
Outcome:	The proposed framework achieves 4% improvement on long-context reasoning benchmarks on Llama and Qwen.

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Jiaxing Sun, Weiquan Huang, Jiang Wu, Chenya Gu, Wei Li, Songyang Zhang, Hang Yan, Conghui He

Rethinking Repetition Problems of LLMs in Code Generation (2025.acl-long)

Challenge:	Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations.
Approach:	They propose to use Chinese commonsense reasoning to evaluate LLMs' commonsensing ability.
Outcome:	The proposed benchmark covers both globally known and Chinese-specific commonsense reasoning abilities and can be used as a reference for future research.

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Yihong Dong, Yuchen Liu, Xue Jiang, Bin Gu, Zhi Jin, Ge Li

Programming Languages in NLP

Challenge:	Recent studies have focused on content repetition, but structural repetition is a more prevalent problem in code generation.
Approach:	They propose a decoding approach that eliminates repetition problems in code generation by identifying grammar rules and strategically decaying the likelihood of critical tokens that contribute to repetitions.
Outcome:	The proposed approach outperforms baselines and humanEval benchmarks on CodeRepetEval dataset and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code.

Pre-Training to Learn in Context (2023.acl-long)

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Yuxian Gu, Li Dong, Furu Wei, Minlie Huang

On the Transformer Growth for Progressive BERT Training (2021.naacl-main)

Challenge:	Pre-trained language models are not explicitly trained to learn in context.
Approach:	They propose a framework to enhance in-context learning by pre-training language models on a large collection of "intrinsic tasks" they evaluate the in-constitution learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark .
Outcome:	The proposed framework outperforms larger language models with nearly 4x parameters on seven widely-used datasets and the Super-NaturalInstrctions benchmark.

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Xiaotao Gu, Liyuan Liu, Hongkun Yu, Jing Li, Chen Chen, Jiawei Han

Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation (2026.acl-long)

Challenge:	Existing methods only conduct network growth in a single dimension, but compound growth operators are beneficial for multiple dimensions.
Approach:	They propose a method to train BERT progressively using a Transformer model and explore alternative growth operators in each dimension via controlled comparison.
Outcome:	The proposed method speeds up BERT pre-training by 73.6% and 82.2% for the base and large models respectively while achieving comparable performances.

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Hao Li, Jiayang Gu, Jingkuan Song, An Zhang, Lianli Gao

HqeKV: Towards Hybrid Quantization and Eviction for KV Cache in Long-Context LLM Inference (2026.findings-acl)

Challenge:	Existing noise mitigation methods face limitations due to their task-specific design, model dependency, and significant computational overhead.
Approach:	They propose a model-agnostic noise label mitigation paradigm that uses an estimator model and a scoring function to assess the noise level of input pairs.
Outcome:	The proposed method is superior to existing methods in training tasks and tasks.

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He Wang, Yu Gu, Fangfang Li, Zhigang Wang, Zhenghao Liu, Ning Wang, Xiaohua Li, Ge Yu

Challenge:	autoregressive inference requires repeated computation across transformer layers.
Approach:	They propose a hybrid compression framework built on both quantization and eviction . they propose varying importance metric and flexible conversion policies to reduce memory overhead .
Outcome:	The proposed framework outperforms state-of-the-art methods under memory constraints.

AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM Coding (2026.findings-acl)

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Samuel Osebe, Fan Yang, Junyi Li, Yue Gu, Yongxin Wang, Satyapriya Krishna, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Weitong Ruan

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) are evolving rapidly on code generation tasks.
Approach:	They propose to automate the vulnerability code benchmark creation with iterative auto validation.
Outcome:	The proposed benchmark covers 232 CWE categories across C/C++, Java, and Python languages.

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan

Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (P18-1)

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.

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Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic

Sentiment Analysis

Challenge:	Existing approaches to classify human affect and subjective information from multiple data sources are limited by the lack of high-level feature associations.
Approach:	They propose a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data.
Outcome:	The proposed model outperforms state-of-the-art approaches on published datasets and visualizes and interprets synchronized attention over modalities.

RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts (2025.acl-long)

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Mingyan Wu, Zhenghao Liu, Yukun Yan, Xinze Li, Shi Yu, Zheni Zeng, Yu Gu, Ge Yu

Text Generation

Challenge:	Retrieval-Augmented Generation (RAG) models enable Large Language Models to access external knowledge.
Approach:	They propose a knowledge refinement method that incorporates reranking signals to generate CoT-based summarization based on query and retrieval documents.
Outcome:	RankCoT generates CoT-based summarization based on query and all retrieval documents . Rank CoT incorporates a self-reflection mechanism that refines the outputs .

Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data (2025.findings-naacl)

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Juanhui Li, Sreyashi Nag, Hui Liu, Xianfeng Tang, Sheikh Muhammad Sarwar, Limeng Cui, Hansu Gu, Suhang Wang, Qi He, Jiliang Tang

High-order Joint Constituency and Dependency Parsing (2024.lrec-main)

Challenge:	Large Language Models (LLMs) have demonstrated superior language understanding abilities in many real-world NLP applications.
Approach:	They propose a learning-based sample selection method that incorporates signals from both teacher and student to enhance model performance.
Outcome:	The proposed method improves model performance across datasets with higher data efficiency.

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Yanggan Gu, Yang Hou, Zhefeng Wang, Xinyu Duan, Zhenghua Li

Syntactic Text Processing

Challenge:	Syntactic parsing aims to reveal how sentences are syntactically structured.
Approach:	They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase .
Outcome:	The proposed model significantly improves matching ratio of whole trees compared to separate models . the proposed model adopts a much more efficient decoding algorithm .

ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)

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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang

Dialogue Systems & Conversational Agents

Challenge:	Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance.
Approach:	They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 .
Outcome:	The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs.

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery (2025.findings-acl)

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

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Hongchao Gu, Dexun Li, Kuicai Dong, Hao Zhang, Hang Lv, Hao Wang, Defu Lian, Yong Liu, Enhong Chen

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)

Challenge:	Existing methods for knowledge-intensive long texts struggle with issues like hallucinations, topic incoherence, and significant latency.
Approach:	They propose a retrieval-augmented long text generation framework with writing Planning and Information to address these challenges.
Outcome:	The proposed framework outperforms state-of-the-art methods on a freshWiki-2024 dataset.

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Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, Ge Li

Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder (2022.emnlp-main)

Challenge:	Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data.
Approach:	They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution.
Outcome:	The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution.

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Zhenghao Liu, Han Zhang, Chenyan Xiong, Zhiyuan Liu, Yu Gu, Xiaohua Li

AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)

Challenge:	Existing work reserves the principle dimensions of query and document embeddings for building more efficient retrieval systems.
Approach:	They propose to use Conditional Autoencoder to compress high-dimensional embeddings to maintain the same embeddable distribution and better recover ranking features.
Outcome:	The proposed algorithm achieves comparable ranking performance with its teacher model and makes the retrieval system more efficient.

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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

Challenge:	Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency.
Approach:	They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images.
Outcome:	The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images.

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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song

EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (2024.lrec-main)

Challenge:	Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach:	They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome:	The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .

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Xue Gu, Zhihan Zhou, Ziyao Meng, Jian Li, Tiago Gomes, Adriano Tavares, Hao Xu

Low-Resource NLP

Challenge:	Existing methods for Emotion-cause pair extraction (ECPE) do not distinguish between the emotion-caused pairs that belong to different types of emotions, limiting their applicability.
Approach:	They propose an Emotion-cause pair extraction method which integrates the implicit knowledge of cause clauses into a prompt template and extends the emotion labels to categories with an external emotion word base.
Outcome:	The proposed method extracts all potential emotion clauses and corresponding cause clauses from unannotated documents.

You Only Need One Single Token to Refine Safety Alignment (2026.findings-acl)

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Wenqian Yu, Shuo Chen, Zhijiang Li, Zhipeng Wang, Jindong Gu

QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)

Challenge:	Excessive safety can lead to over-refusal, where models reject harmful-looking yet benign queries, severely limiting utility.
Approach:	They propose a lightweight training-based approach that reshapes the distributions of harmful and benign samples within the model’s decision space by using a single-token prefix.
Outcome:	The proposed approach can distinguish between harmful and benign samples while keeping the model frozen.

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Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo

Challenge:	Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving.
Approach:	They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch.
Outcome:	The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput.

DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)

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Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu

CoMMIT: Coordinated Multimodal Instruction Tuning (2025.emnlp-main)

Challenge:	Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data.
Approach:	They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs.
Outcome:	The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback.

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Xintong Li, Junda Wu, Tong Yu, Rui Wang, Yu Wang, Xiang Chen, Jiuxiang Gu, Lina Yao, Julian McAuley, Jingbo Shang

Hybrid Attention based Multimodal Network for Spoken Language Classification (C18-1)

Challenge:	et al., 2024) show that multimodal instruction tuning is more effective than baselines.
Approach:	They propose a multimodal balance coefficient that enables quantitative measurement of the balance of learning . they propose auxiliary regularization on the gradient to promote updating with larger step sizes .
Outcome:	The proposed method is more effective than baselines in MLLM instruction tuning.

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Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic

Information Extraction & Text Mining

Challenge:	Using linguistic content and vocal characteristics for multimodal deep learning is difficult for computers to interpret human meaning .
Approach:	They propose a deep multimodal network with feature attention and modality attention to classify utterance-level speech data.
Outcome:	The proposed system achieves state-of-the-art or competitive results on three published multimodal datasets.

Long-Chain Reasoning Distillation via Adaptive Prefix Alignment (2026.acl-long)

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Zhenghao Liu, Zhuoyang Wu, Xinze Li, Yukun Yan, Shuo Wang, Zulong Chen, Yu Gu, Ge Yu, Maosong Sun

SPEAK: Spiking Neurons as an Entropy-Aware Tokenizer for Large Language Models (2026.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, especially in solving complex mathematical problems.
Approach:	They propose a framework that exploits teacher CoTs for distillation through adaptive prefix alignment.
Outcome:	The proposed framework outperforms baseline models on multiple mathematical reasoning benchmarks by over 3%.

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Ming Chen, Wenyao Li, Chao Liang, Shi Gu, Peng Lin, De Ma, Huajin Tang, Qian Zheng, Gang Pan

Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)

Challenge:	Existing tokenizers fail to explicitly leverage historical tokenization results . large language models (LLMs) have demonstrated remarkable effectiveness across NLP tasks .
Approach:	They propose a tokenizer that integrates spiking neurons to explicitly leverage historical tokenization results.
Outcome:	The proposed tokenizer leverages historical tokenization results, but does not selectively leverage history based on contextual relevance.

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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong

Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models (2026.acl-long)

Challenge:	Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes.
Approach:	They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer.
Outcome:	The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks.

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Tingchen Fu, Yafu Li, Jiawei Gu, Xiaoye Qu, Yu Cheng

METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling (2025.acl-long)

Challenge:	Recent advances in reasoning-oriented models have demonstrated impressive capabilities in mathematical reasoning, but their ability to adhere to user directives remains underexplored.
Approach:	They propose a benchmark to evaluate instruction-following in mathematical reasoning tasks.
Outcome:	The proposed model degrades in instruction adherence when generation length increases, but can partially recover obedience, despite increasing generation length.

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Bingxuan Li, Yiwei Wang, Jiuxiang Gu, Kai-Wei Chang, Nanyun Peng

Few-shot In-context Learning on Knowledge Base Question Answering (2023.acl-long)

Challenge:	Chart generation requires strong visual design skills and precise coding capabilities that embed the desired visual properties into code.
Approach:	They propose a vision-language model-based multi-agent framework for effective automatic chart generation.
Outcome:	The proposed framework achieves a 5.2% improvement in the F1 score over the current best chart generation task.

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Tianle Li, Xueguang Ma, Alex Zhuang, Yu Gu, Yu Su, Wenhu Chen

T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)

Challenge:	KB-BINDER enables few-shot in-context learning over knowledge base questions . KBQA is a difficult problem due to the heterogeneity of knowledge bases .
Approach:	They propose a framework that enables few-shot in-context learning over KBQA tasks.
Outcome:	The proposed framework can outperform state-of-the-art models on GraphQA and MetaQA datasets.

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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li

Structured Data in NLP

Challenge:	Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications.
Approach:	They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning.
Outcome:	The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation.

A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)

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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova

Towards Efficient and Effective Diffusion Language Model Inference via Semantic-Aware Adaptive Denoising (2026.acl-long)

Challenge:	Existing document understanding models focus on single-modal inputs such as images or texts.
Approach:	They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information.
Outcome:	The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines.

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Fan Li, Yu Gu, Zhigang Wang, Fangling Leng, Zhenghao Liu, Ge Yu

COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis (2025.findings-naacl)

Challenge:	Existing acceleration works cannot accurately detect semantically stabilized tokens and then skip computation, leading to sub-optimal speedup in practice.
Approach:	They propose a semantic-aware adaptive denoising framework that encodes scalar confidence scores into an evolution-awful feature vector and clusters vectors proactively and adaptively identify semantically converged tokens.
Outcome:	The proposed framework outperforms the SOTA competitor in speed and quality . it can detect semantically stabilized tokens and skip computation, resulting in sub-optimal speedup .

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Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators (2026.findings-acl)

Challenge:	Existing code debugging benchmarks focus on the Code Repair stage of the code generation process.
Approach:	They propose a framework to evaluate the debugging abilities of large language models by emulating the human debug process.
Outcome:	The proposed framework outperforms human-curated and GPT-4-generated training data, enabling 7B-scale LLMs to achieve comparable debugging performance to GPT-3.5.

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Feng Gu, Zongxia Li, Carlos R. Colon, Benjamin Evans, Ishani Mondal, Jordan Lee Boyd-Graber

Information Extraction & Text Mining

Challenge:	State-of-the-art NLP models are expensive and inefficient for event annotation.
Approach:	They propose to integrate LLMs into a holistic workflow that summarizes news with event coreference resolution and argument extraction in three modes: AI-only, AI assistance, and human only.
Outcome:	The proposed workflow integrates LLMs to alleviate human labor in a holistic pipeline.

Explain-Analyze-Generate: A Sequential Multi-Agent Collaboration Method for Complex Reasoning (2025.coling-main)

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WenYuan Gu, JiaLe Han, HaoWen Wang, Xiang Li, Bo Cheng

Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders (2021.eacl-main)

Challenge:	Multiagent debate (MAD) is a popular approach for large language models . however, the performance of LLMs is suboptimal in complex reasoning scenarios .
Approach:	They propose a sequential collaboration framework to enable agents to provide constructive assistance to peers by decomposing complex tasks into essential subtasks.
Outcome:	The proposed framework achieves the highest performance on 19 out of 23 tasks and lower costs compared to MAD.

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Xiang Kong, Adithya Renduchintala, James Cross, Yuqing Tang, Jiatao Gu, Xian Li

MHALO: Evaluating MLLMs as Fine-grained Hallucination Detectors (2025.findings-acl)

Challenge:	Recent work in multilingual translation has improved translation quality surpassing bilingual baselines using deep transformer models with increased capacity.
Approach:	They propose a deep encoder with multiple shallow decoders to reduce inference latency while maintaining translation quality.
Outcome:	The proposed model achieves 1.8x speedup on average compared to a standard transformer model with no drop in translation quality.

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Yishuo Cai, Renjie Gu, Jiaxu Li, Xuancheng Huang, Junzhe Chen, Xiaotao Gu, Minlie Huang

MASH: Evading Black-Box AI-Generated Text Detectors via Style Humanization (2026.findings-acl)

Challenge:	Hallucination remains a critical challenge for multimodal large language models, undermining their reliability in real-world applications.
Approach:	They propose a benchmark specifically designed for evaluating MLLMs’ capability in performing token-level hallucination detection (FHD) . they use curated training data to train a specialized model that significantly outperforms existing models.
Outcome:	The proposed model outperforms existing models in the evaluation of 9 MLLMs and reaches an average F1IoU of 40.59%.

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Yongtong Gu, Songze Li, Xia Hu

Robustness in NLP

Challenge:	Existing detection methods rely on white-box assumptions or require prohibitively high computational and interaction costs, rendering them ineffective under practical black-box scenarios.
Approach:	They propose a framework that evades black-box detection methods based on style transfer by using style-injection supervised fine-tuning and direct preference optimization to shape distributions of AI-generated texts to resemble those of human-written texts.
Outcome:	The proposed framework achieves an average Attack Success Rate (ASR) of 92%, surpassing the strongest baselines by an average of 24% while maintaining superior linguistic quality.

Can an Individual Manipulate the Collective Decisions of Multi-Agents? (2025.emnlp-main)

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Fengyuan Liu, Rui Zhao, Shuo Chen, Guohao Li, Philip Torr, Lei Han, Jindong Gu

Robustness in NLP

Challenge:	Recent studies show that coordinated multi-agent systems exhibit enhanced decision-making and reasoning abilities through collaboration.
Approach:	They propose a framework that simulates agent interactions within a multi-agent system to generate adversarial samples and use them to manipulate the target agent in the target system.
Outcome:	The proposed framework generates adversarial samples that are used to manipulate the target agent in the target system, misleading the system’s decision-making process.

Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)

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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen

A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task (C18-1)

Challenge:	despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination.
Approach:	They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods.
Outcome:	The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation .

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Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, Zhenglu Yang

Knowledge Representation

Challenge:	Existing studies on the topic of common sense story understanding focus on generating guesses for a missing event or concentrating on unsupervised learning.
Approach:	They propose to extend attention-based neural network with external knowledge resources to understand temporal stories and predict their endings.
Outcome:	The proposed model outperforms state-of-the-art models and external knowledge resources.

Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations (P19-1)

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Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O.K. Li

How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs (2025.findings-acl)

Challenge:	Existing approaches to train a multilingual NMT model for low-resource languages are lacking in terms of number of supervised examples.
Approach:	They propose to use decoder pre-training and back-translation to solve the degeneracy problem by analyzing spurious correlations between source and decoded sentences.
Outcome:	The proposed methods show significant improvement over the pivot-based approach on three challenging multilingual datasets.

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Guhao Feng, Kai Yang, Yuntian Gu, Xinyue Ai, Shengjie Luo, Jiacheng Sun, Di He, Zhenguo Li, Liwei Wang

Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)

Challenge:	Despite the success of transformer-based large language models, understanding and enhancing their mathematical capabilities remains a significant challenge.
Approach:	They propose to use numerical precision as a key factor that influences LLMs' effectiveness in arithmetical tasks to determine their effectiveness.
Outcome:	The proposed models perform better in arithmetic tasks than transformer-based models with standard numerical precision.

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Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino

Text Generation

Challenge:	Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations.
Approach:	They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition.
Outcome:	The proposed method achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task.

MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)

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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin