Papers by Shuo Li

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

Challenge:	a large number of large language models are being used to protect user privacy . sanitizing sensitive text using two common strategies is the answer .
Approach:	They propose sanitizing sensitive text using deleting expressions and abstracting them . they propose a tool for text rewriting that uses crowdsourcing and large language models .
Outcome:	The proposed approach protects privacy before sending sensitive data to large language models . it combines crowdsourcing and large language modeling to create a text rewrite tool .

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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Text Generation

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning (2025.naacl-long)

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Hanqing Wang, Yixia Li, Shuo Wang, Guanhua Chen, Yun Chen

OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory (2026.acl-long)

Challenge:	Efficient finetuning of large language models (LLMs) aims to adapt the LLMs with reduced computational and memory costs.
Approach:	They propose a simple yet effective method that initializes low-rank matrices with Gaussian distribution and zero values while keeping the original weight matrics frozen.
Outcome:	The proposed approach only updates the minor components of the weight matrix while keeping the principal singular components frozen.

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Jinze Li, Yang Zhang, Xin Yang, Jiayi QU, Jinfeng Xu, Shuo Yang, Junhua Ding, Edith Cheuk-Han Ngai

Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning (2022.findings-emnlp)

Challenge:	Existing LLMs are limited by text-context budgets, resulting in token-expensive storage of raw trajectories . Optical Context Retrieval Memory (OCR-Memory) renders historical tra-jectorios into images annotated with unique visual identifiers.
Approach:	They propose a framework that leverages the visual modality as a high-density representation of agent experience.
Outcome:	Optical Context Retrieval Memory (OCRM) renders historical trajectories into images annotated with unique visual identifiers.

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Shuo Xie, Jiahao Qiu, Ankita Pasad, Li Du, Qing Qu, Hongyuan Mei

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

Challenge:	Existing approaches to transfer a pretrained language model include fine-tuning all the parameters in the language model and adapting all its subsets.
Approach:	They propose to select layers based on the variability of their hidden states given a task-specific corpus.
Outcome:	The proposed model reduces the computational cost of transfer learning methods without sacrificing performance.

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang

Text Generation

Challenge:	Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain.
Approach:	They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process.
Outcome:	The proposed dataset evaluates the performance of unsupervised methods and advanced large language models.

LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN (2026.findings-acl)

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Sijie Wang, Kai Zhao, Wee Peng Tay, Shuo Zhang, Chengwen Liu, Quanjiang Guo, Ren Junhao, Xin Li, Heng Lian, Jingdi Lei, Rui She, Huacan Wang, Ronghao Chen

Challenge:	Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding.
Approach:	They propose an SWE-level benchmark for AI coding in the Huawei Ascend CANN software stack.
Outcome:	The proposed benchmark is constructed from real-world CANN repositories and consists of over 400 task instances spanning multiple file, multi-language, and execution-aware coding challenges.

QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization (2026.acl-long)

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Changxin Ke, Rui Zhang, Jiaming Guo, Yuanbo Wen, Li Ding, Shuo Wang, Xuyuan Zhu, Xiong Peng, Di Huang, Zidong Du, Xing Hu, Qi Guo, Yunji Chen

AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing (2026.findings-acl)

Challenge:	Existing approaches to program repair are based on correctness alone.
Approach:	They propose a framework that mitigates over-editing and improves repair accuracy by generating buggy programs and re-edits.
Outcome:	The proposed framework improves repair precision by 31.4% under fix1@1, a metric that considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing.

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Zhenhua Xu, Dongsheng Chen, Shuo Wang, Jian Li, Chengjie Wang, Meng Han, Yabiao Wang

Challenge:	Existing LLMs lack immersion and adaptability, resulting in limited character orchestration and on-the-fly character introduction.
Approach:	They propose an LLM-based framework that allows actors to interact with users in an ongoing narrative.
Outcome:	The proposed framework outperforms commercial LLMs in character consistency, environment grounding, and narrative coherence.

Generative Bridging Network for Neural Sequence Prediction (N18-1)

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Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou

Text Generation

Challenge:	Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing.
Approach:	They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence.
Outcome:	The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden.

Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)

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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang

TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction (2024.naacl-long)

Challenge:	Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities.
Approach:	They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion .
Outcome:	The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT .

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Shuo Li, Sangdon Park, Insup Lee, Osbert Bastani

Uncertainty in Language Models: Assessment through Rank-Calibration (2024.emnlp-main)

Challenge:	Large language models (LLMs) often generate incorrect responses based on made-up facts, which are called hallucinations.
Approach:	They propose a framework that combines Retrieval Augmented Generation with conformal prediction to provide the first end-to-end statistical correctness guarantee for RAG.
Outcome:	The proposed framework reduces prediction set size by 16.2% on average compared to an ablation.

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Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban

MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety (2025.emnlp-main)

Challenge:	Language Models (LMs) have shown promising performance in natural language generation . however, it is crucial to correctly quantify their level of uncertainty in responding to inputs.
Approach:	They propose a framework to quantify uncertainty and confidence for Large Language Models . they use a Rank-calibration framework to measure uncertainty and confident responses .
Outcome:	The proposed framework assesses uncertainty and confidence measures for LMs.

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Yahan Yang, Soham Dan, Shuo Li, Dan Roth, Insup Lee

Nuclear Deployed!: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking, which can elicit harmful or unsafe behaviors.
Approach:	They propose a multilingual guardrail with reasoning for prompt classification that integrates culturally and linguistically nuanced variants and supervised fine-tuning.
Outcome:	The proposed guardrail outperforms baselines across in-domain and out-of-domain languages by more than 15%.

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Rongwu Xu, Xiaojian Li, Shuo Chen, Wei Xu

Challenge:	Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes domains, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) .
Approach:	They propose a framework that is carefully constructed to effectively and naturally expose catastrophic risks in high-stakes domains such as CBRN.
Outcome:	The proposed framework exposes LLM agents to catastrophic behaviors and deception without being deliberately induced.

Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric (2025.acl-long)

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Yuming Yang, Yang Nan, Junjie Ye, Shihan Dou, Xiao Wang, Shuo Li, Huijie Lv, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance.
Approach:	They propose to use data diversity to measure instruction tuning of large language models.
Outcome:	The proposed diversity metric outperforms existing methods on simulated and real-world data and shows that it captures diversity variations and achieves a 0.97 correlation with instruction tuning.

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

Spec-o3: A Tool-Augmented Vision-Language Agent for Rare Celestial Object Candidate Vetting via Automated Spectral Inspection (2026.acl-long)

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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Minghui Jia, Qichao Zhang, Ali Luo, Linjing Li, Shuo Ye, Hailing Lu, Wen Hou, Dongbin Zhao

Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Spec-o3 is a tool-augmented vision-language agent that performs astronomer-aligned spectral inspection.
Approach:	They propose a tool-augmented vision-language agent that performs astronomer-aligned spectral inspection via interleaved multimodal chain-of-thought reasoning.
Outcome:	Spec-o3 outperforms traditional visual inspection methods on rare-object inspection tasks.

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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage (2026.acl-long)

Challenge:	Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections.
Approach:	They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models.
Outcome:	The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages.

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Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi, Weilun Zhao, Shuo Wang, Duzhen Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Challenge:	Efficient reproduction of research papers requires deep domain expertise.
Approach:	They propose a framework that systematically mines implicit knowledge from the cited literature to reproduce experimental code in a complete, end-to-end manner.
Outcome:	The proposed framework surpasses baselines across all metrics and reproduces experimental code in a complete, end-to-end manner.

MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)

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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang

MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria (2025.naacl-long)

Challenge:	Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature.
Approach:	They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding.
Outcome:	The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M .

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Wentao Ge, Shunian Chen, Hardy Chen, Nuo Chen, Junying Chen, Zhihong Chen, Wenya Xie, Shuo Yan, ChenghaoZhu ChenghaoZhu, Ziyue Lin, Dingjie Song, Xidong Wang, Anningzhe Gao, Zhang Zhiyi, Jianquan Li, Xiang Wan, Benyou Wang

RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine (2026.findings-acl)

Challenge:	Existing evaluation methodologies for multimodal large language models are limited in evaluating objective queries without considering real-world user experiences.
Approach:	They propose to evaluate multimodal large language models with per-sample criteria using potent MLLM as the judge.
Outcome:	The proposed evaluation paradigm shows that it can be used to evaluate multimodal large language models with per-sample criteria.

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Jiatan Huang, Mingchen Li, Zonghai Yao, Dawei Li, Yuxin Zhang, Zhichao Yang, Yongkang Xiao, Feiyun Ouyang, Xiaohan Li, Shuo Han, Hong yu

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

Challenge:	Existing methods for retrieving medical textual knowledge Graphs struggle to perform well, a study finds . existing methods struggle to provide accurate answers to complex questions, he says .
Approach:	They synthesize user queries integrating diverse topological structures, relational information, and complex textual descriptions.
Outcome:	a new dataset for medical textual knowledge graphs shows that existing methods struggle to perform well . main bottlenecks lie in the scarcity of existing medical TKGs and the limited expressiveness of their topological structures .

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

Pluggable Neural Machine Translation Models via Memory-augmented Adapters (2024.lrec-main)

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Yuzhuang Xu, Shuo Wang, Peng Li, Xuebo Liu, Xiaolong Wang, Weidong Liu, Yang Liu

Machine Translation

Challenge:	Recent years, neural machine translation systems are often developed with large-scale parallel data extracted from the Web.
Approach:	They propose a memory-augmented adapter to steer pretrained neural machine translation models in a pluggable manner by combining model representations and retrieved results.
Outcome:	The proposed method outperforms several representative pluggable baselines on style- and domain-specific experiments.

EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection (2026.acl-long)

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Shuo Yang, Caren Han, Xueqi Ma, Yan Li, Mohammad Reza Ghasemi Madani, Eduard Hovy

Challenge:	Existing approaches to optimize tool-use policies are monolithic and prone to entangling behaviors.
Approach:	They propose a framework that decomposes agent’stool-use policy into four modules and improves them via three mechanisms.
Outcome:	The proposed framework outperforms strong baselines on bothGPT-4.1 and Qwen3-8B while maintaining superior efficiency and transferability.

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

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

A Secure and Efficient Federated Learning Framework for NLP (2021.emnlp-main)

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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Chenghong Wang, Jieren Deng, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding

Green & Sustainable NLP

Challenge:	Existing FL frameworks require a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded.
Approach:	They propose a framework that is federated and efficient for NLP . they propose to eliminate the need for trusted entities and achieve better model accuracy .
Outcome:	The proposed framework achieves better model accuracy and model accuracy than existing FL frameworks.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)

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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu

Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction (2025.naacl-short)

Challenge:	Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem .
Approach:	They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths .
Outcome:	The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines .

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Xi Chen, Mao Mao, Shuo Li, Haotian Shangguan

Green & Sustainable NLP

Challenge:	Comparative experiments show that our model outperforms several general-purpose and domain-specific legal models.
Approach:	They propose a legal judgment prediction model that integrates LLMs with argumentative reasoning techniques to simulate the debate phase of real courtroom trials.
Outcome:	The proposed model outperforms several general-purpose and domain-specific legal models and offers a dynamic reasoning process.

Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)

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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu

Challenge:	Positional biases in large language models hinder their ability to process long inputs.
Approach:	They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information.
Outcome:	The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks.

AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)

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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu

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

Challenge:	Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities.
Approach:	They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness .
Outcome:	Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds .

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

Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages (2024.acl-long)

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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Yuanchi Zhang, Yile Wang, Zijun Liu, Shuo Wang, Xiaolong Wang, Peng Li, Maosong Sun, Yang Liu

RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction (2022.emnlp-main)

Challenge:	Contemporary large language models (LLMs) are pre-trained on multilingual corpora, but their performance lags behind in most languages compared to a few resource-rich languages.
Approach:	They propose a method that leverages the internal capabilities of large language models on resource-rich languages to enhance multilingual performance.
Outcome:	The proposed method improves multilingual performance while minimizing impact on original performance in resource-rich languages.

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Zhoujin Tian, Chaozhuo Li, Shuo Ren, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Denvy Deng, Qi Zhang, Xing Xie

Triangular Architecture for Rare Language Translation (P18-1)

Challenge:	Existing approaches focus on minimizing distances between words in aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates.
Approach:	They propose a ranking-oriented induction model to learn personalized mapping function for each word.
Outcome:	The proposed model can learn personalized mapping function for each word on public datasets including rich-resource and low-resourced languages.

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Shuo Ren, Wenhu Chen, Shujie Liu, Mu Li, Ming Zhou, Shuai Ma

Making Long-Context Language Models Better Multi-Hop Reasoners (2024.acl-long)

Challenge:	Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language.
Approach:	They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs.
Outcome:	Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods.

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Yanyang Li, Shuo Liang, Michael Lyu, Liwei Wang

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

Challenge:	Recent advances in long-context modeling have enhanced language models for complex tasks, but they struggle with multi-hop reasoning and noisy contexts.
Approach:	They propose an approach that prompts LMs to supply attributions for each assertion during reasoning.
Outcome:	The proposed model achieves competitive performance on multi-hop reasoning benchmarks, closely paralleling proprietary LMs such as ChatGPT and Claude-instant.

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

DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents (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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JunShuo Zhang, Chengrui Huang, Feng Guo, Zihan Li, Ke Shi, Menghua Jiang, Jiguo Yu, Shuo Shang, Shen Gao

TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)

Challenge:	Existing approaches to large language model (LLM) agents that follow the sequential "reason-then-act" paradigm suffer from limited exploration and incomplete environmental understanding as they interact with only a single environment per step.
Approach:	They propose a paradigm that enables an agent to interact with multiple environments simultaneously and share cross-trajectory experiences.
Outcome:	The proposed paradigm achieves state-of-the-art (SOTA) success rates while maintaining comparable efficiency to strong sequential baselines.

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Yinghao Li, Xuebo Liu, Shuo Wang, Peiyuan Gong, Derek F. Wong, Yang Gao, Heyan Huang, Min Zhang

Syntactic Text Processing

Challenge:	Existing methods for grammatical error correction (GEC) have been developed.
Approach:	They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input.
Outcome:	The proposed method can perform human-in-the-loop error correction tasks.

LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research (2025.emnlp-main)

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Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, Juntong Ni, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

Challenge:	Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery, but their capability in reproducing code from research papers remains underexplored.
Approach:	They propose to evaluate LLM agents' ability to reproduce scientific research papers by analyzing code reproduction tasks from 23 research papers published in top-tier NLP venues.
Outcome:	The proposed benchmark systematically evaluates the capability of large language model (LLM) agents on code reproduction from Language Modeling Research.

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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)

Challenge:	Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
Approach:	They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow.
Outcome:	The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow.

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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

Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels (2025.emnlp-main)

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.

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Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan

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

Challenge:	Large language models (LLMs) acquire substantial world knowledge during pretraining, which is further shaped by post-training techniques such as supervised fine-tuning (SFT).
Approach:	They evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLama-3 families and examine the impact of supervised fine-tuning on model knowledge.
Outcome:	The proposed model performance is 14% worse than models fine-tuned on 1,920 samples and 12% worse on 240 samples.

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

Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization (2025.emnlp-main)

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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Shuo Xing, Peiran Li, Yuping Wang, Ruizheng Bai, Yueqi Wang, Chan-Wei Hu, Chengxuan Qian, Huaxiu Yao, Zhengzhong Tu

Few-Shot Semantic Parsing for New Predicates (2021.eacl-main)

Challenge:	emergence of large Vision Language Models (VLMs) has broadened the capabilities of single-modal Large Language Model (LLM) but VLMs are prone to significant hallucinations, especially in the form of cross-modal inconsistencies.
Approach:	They propose a new alignment framework that leverages image retrieval to integrate both textual and visual preference signals.
Outcome:	The proposed framework mitigates hallucinations more effectively than previous methods . it maintains robustness and scalability across a wide range of VLM sizes and architectures .

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Zhuang Li, Lizhen Qu, Shuo Huang, Gholamreza Haffari

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)

Challenge:	a recent study shows that state-of-the-art neural semantic parsers are less accurate when there is only a handful of utterance-logical form pairs per predicate.
Approach:	They propose to use a meta-learning method to train a few-shot learning problem . they also propose to regularize attention scores with alignment statistics and apply a smoothing technique .
Outcome:	The proposed method outperforms baselines in one and two-shot settings.

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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya

DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

Challenge:	Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA .
Approach:	They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities.
Outcome:	a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region .

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Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang, Fuwen Luo, Weizheng Qiao, Lu Han, Ziwei Sun, Peng Li, Yang Liu

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)

Challenge:	Dongba pictographic is the only pictograph script still in use in the world.
Approach:	DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome:	The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

Challenge:	Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations.
Approach:	They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference.
Outcome:	The proposed method significantly mitigates object hallucinations across various model architectures.

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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun

On Robustness of Neural Semantic Parsers (2021.eacl-main)

Challenge:	Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size.
Approach:	They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding.
Outcome:	The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models.

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Shuo Huang, Zhuang Li, Lizhen Qu, Lei Pan

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

Challenge:	Semantic parsing maps natural language (NL) utterances into logical forms (LFs) adversarial examples are created by adding tiny perturbations to inputs but can severely deteriorate model performance.
Approach:	They propose to construct robustness test sets based on existing benchmark corpora and to evaluate the effect of data augmentation.
Outcome:	The proposed method measures the performance of the proposed parsers on robustness test sets and evaluates the effect of data augmentation.

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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.

A Template-based Method for Constrained Neural Machine Translation (2022.emnlp-main)

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Shuo Wang, Peng Li, Zhixing Tan, Zhaopeng Tu, Maosong Sun, Yang Liu

360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System (2024.findings-acl)

Challenge:	Existing methods to solve this problem can not satisfy the following three desiderata: (1) high translation quality, (2) high match accuracy, and (3) low latency.
Approach:	They propose a template-based method that can provide high translation quality and match accuracy and a low latency inference.
Outcome:	The proposed method outperforms baselines in lexically and structurally constrained translation tasks and can be used in a variety of applications.

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Shen Gao, Hao Li, Zhengliang Shi, Chengrui Huang, Quan Tu, Shuo Shang, Zhiliang Tian, Minlie Huang

Is Parameter Collision Hindering Continual Learning in LLMs? (2025.coling-main)

Challenge:	Recent studies focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks.
Approach:	They propose a hierarchical multi-agent framework that uses 360 assessment to accumulate experience through fine-grained assessment.
Outcome:	The proposed framework is based on corporate organizational practices and employs a dual-level experience pool for agents to accumulate experience through fine-grained assessment.

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Shuo Yang, Kun-Peng Ning, Yu-Yang Liu, Jia-Yu Yao, Yong-Hong Tian, Yi-Bing Song, Li Yuan