Papers by Fang Li

DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding (2026.findings-acl)

Challenge:	Recent work explicitly decomposes the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively.
Approach:	They propose a non-parallelelizable table-to-text model that produces outputs in parallel with one network.
Outcome:	The proposed model achieves 3.0 5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors.

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Guanghao Li, Zhihui Fu, Min Fang, Qibin Zhao, Ming Tang, Chun Yuan, Jun Wang

OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)

Challenge:	Autoregressive (AR) decoding in large language models is latency-bounded by strictly sequential token generation.
Approach:	They propose a diffusion-based drafter that proposes multi-token candidates and then verifies them in parallel by the target model.
Outcome:	The proposed drafter generates multi-token proposals in a single forward pass while remaining compatible with standard AR verifiers.

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Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li

Class Name Guided Out-of-Scope Intent Classification (2024.findings-emnlp)

Challenge:	Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios.
Approach:	They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency.
Outcome:	The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses.

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Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Sahu, Xiaoli Li

Model Composition for Multimodal Large Language Models (2024.acl-long)

Challenge:	SCOOS leverages semantic cues embedded in class labels to improve classification accuracy.
Approach:	They propose a method to create a compact feature space around class label semantics . they use a shared latent space between ID features and class names to minimize losses .
Outcome:	The proposed method outperforms existing methods for out-of-scope intent detection and ID intent classification.

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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu

TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency (2025.acl-long)

Challenge:	Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities.
Approach:	They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters.
Outcome:	The proposed model retains the modal understanding capabilities of each original model.

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Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu

IntentCoding: Amplifying User Intent in Code Generation (2026.findings-acl)

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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Zheng Fang, Yihong Dong, Lili Mou, Dongming Jin, Zhi Jin, Ge Li

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

Challenge:	IntentCoding captures the influence of user intent by masking out the intent, and integrates seamlessly with existing decoding procedures.
Approach:	They propose a decoding strategy that captures the influence of user intent by masking out the intent and applies a multi-strength ensemble mechanism to amplify the effect of user intention during generation.
Outcome:	The proposed model significantly improves both constraint satisfaction and functional correctness compared to greedy decoding approaches.

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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.

TrInk: Ink Generation with Transformer Network (2025.emnlp-main)

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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu

CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)

Challenge:	Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples.
Approach:	They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies.
Outcome:	The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models.

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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang

Multi-Hop Question Generation via Dual-Perspective Keyword Guidance (2025.findings-acl)

Challenge:	Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks.
Approach:	They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process.
Outcome:	The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless"

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Maodong Li, Longyin Zhang, Fang Kong

RFS-Guard: Detecting Reasoning Hallucinations via Cross-Phase Routing Focus in Large Reasoning Models (2026.acl-long)

Challenge:	Existing work fails to fully utilize the guiding potential of keywords and neglect to differentiate the distinct roles of question-specific and document-specific keywords.
Approach:	They propose a dual-perspective keyword-guided framework that integrates question and document keywords into the multi-hop question generation process.
Outcome:	The proposed framework integrates question and document keywords into the multi-hop question generation process.

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Zihang Liu, Fang Zhouhua, Hui Liu, Zhiwei Liu, Yong Li, Haishuai Wang

Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting (2023.findings-emnlp)

Challenge:	Large reasoning models (LRMs) generate intermediate reasoning traces before the final answer, yet they remain vulnerable to reasoning hallucinations such as subtle arithmetic errors.
Approach:	They propose a Routing Focus Score (RFS) that measures how strongly cross-step attention routing aligns with semantic proximity derived from hidden-state cosine similarity.
Outcome:	The proposed framework detects and localizes hallucinations without external tools or repeated sampling.

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Fanghua Ye, Meng Fang, Shenghui Li, Emine Yilmaz

Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (2025.acl-long)

Challenge:	Existing approaches to rewrite context-dependent queries lack sufficient information for optimal retrieval performance.
Approach:	They propose to use large language models (LLMs) as query rewriters to generate informative queries through well-designed instructions.
Outcome:	The proposed approach improves performance on the QReCC dataset compared to human rewrites .

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Xiaowei Zhu, Yubing Ren, Yanan Cao, Xixun Lin, Fang Fang, Yangxi Li

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)

Challenge:	Existing methods focus excessively on detection accuracy, neglecting the societal risks posed by high false positive rates (FPRs).
Approach:	They propose a Conformal Prediction framework that constrains the upper bound of false positive rates and introduces a real-time detection framework.
Outcome:	The proposed framework reduces false positive rates and improves detection performance.

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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian

Context-DPO: Aligning Language Models for Context-Faithfulness (2025.findings-acl)

Challenge:	LLM-based agents for machine learning engineering rely on tree search to rank candidates.
Approach:	They propose an LLM-based agent that operationalizes gradient-based optimization.
Outcome:	The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU.

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Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang, Zihan Zhang, Haizhen Huang, Lingrui Mei, Junfeng Fang, Zehao Li, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Shenghua Liu

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval (2021.naacl-main)

Challenge:	Context-DPO is the first alignment method specifically designed to enhance contextfaithfulness for large language models.
Approach:	They propose a benchmark that simulates Retrieval-Augmented Generation scenarios with knowledge conflicts to evaluate context-faithfulness.
Outcome:	The proposed method improves LLMs' context-faithfulness by 35% to 280% over open-source models.

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Siqi Sun, Yen-Chun Chen, Linjie Li, Shuohang Wang, Yuwei Fang, Jingjing Liu

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

Challenge:	Existing pre-trained models suffer from slow inference speed due to cross-modal attention in transformer architecture.
Approach:	They propose a multimodal approach that accelerates the inference time of ITR by thousands of times . they extract pre-cached feature indexes offline and employ instant dot-product matching online .
Outcome:	The proposed approach outperforms existing models that consume 1000 times magnitude of computational hours using the same features.

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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

ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection (2026.findings-acl)

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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Boyang Li, Hongzhe Shou, Yuanyuan Liang, JingBin Zhang, Fang Zhou

LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment (2026.findings-acl)

Challenge:	Existing toxic content detection methods focus on sentence-level classification but fail to provide readable and contiguous toxic evidence spans.
Approach:	They propose an explainability-oriented method for Chinese toxic content detection methods . they refine saliency cues into fine-grained toxic spans with lightweight LLM guidance .
Outcome:	The proposed method improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent explanations.

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Shipeng Li, Zhiqin Yang, Shikun Li, Xiaobo Xia, Hengyu Liu, Xinghua Zhang, Gaode Chen, Dong Fang, Ying Tai, Zhe Peng

A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure (2020.acl-main)

Challenge:	Reinforcement learning with verifiable rewards (RLVR) is a key technique for enhancing LLMs’ reasoning abilities, yet its data inefficiency remains a major bottleneck.
Approach:	They propose a gradient-alignment-based method which intelligently selects the learnable and representative training reasoning data for RLVR post-training.
Outcome:	Experiments on five reasoning benchmarks show that the proposed method significantly reduces training data requirements while improving performance.

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Longyin Zhang, Yuqing Xing, Fang Kong, Peifeng Li, Guodong Zhou

Attack as Defense: Safeguarding Large Vision-Language Models from Jailbreaking by Adversarial Attacks (2025.findings-emnlp)

Challenge:	Text-level discourse parsing of discourse rhetorical structure (DRS) is a fundamental research topic in natural language processing.
Approach:	They propose a top-down neural architecture for text-level discourse parsing . they cast the parser as a recursive split point ranking task .
Outcome:	The proposed top-down approach is more suitable for text-level discourse parsing.

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Chongxin Li, Hanzhang Wang, Yuchun Fang

Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)

Challenge:	adversarial vulnerabilities in vision-language systems pose a challenge to reliability of large systems . typographic manipulations and adversarial perturbations can bypass language model defenses .
Approach:	They propose a method that embeds perturbations in vision to disrupt attacks . they use cross-modal interactions to enhance adversarial robustness through perturbations .
Outcome:	The proposed approach reduces attack success rates for typographic attacks and adversarial perturbations by integrating visual defenses into the model.

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Guangtao Lyu, Qi Liu, Chenghao Xu, Jiexi Yan, Muli Yang, Xueting Li, Fen Fang, Cheng Deng

Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading (2020.coling-main)

Challenge:	Existing training-free methods are vulnerable to the attention sink phenomenon . Existing methods include contrastive decoding and auxiliary expert models .
Approach:	They propose a training-free attention intervention that constructs a PAD map to identify semantically core visual regions and applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength.
Outcome:	The proposed intervention improves visual grounding and reduces hallucinations on multiple LVLMs and benchmarks.

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Feng Jiang, Xiaomin Chu, Peifeng Li, Fang Kong, Qiaoming Zhu

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

Challenge:	Existing methods on discourse parsing in English suffer from long discourse units and fewer explicit connectives.
Approach:	They propose to use two reading modes to construct Chinese paragraph level discourse trees.
Outcome:	The proposed model outperforms baselines on Chinese discourse trees.

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

EXPLAIN: Enhancing Retrieval-Augmented Generation with Entity Summary (2025.acl-industry)

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

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Yaozhen Liang, Xiao Liu, Jiajun Yu, Zhouhua Fang, Qunsheng Zou, Linghan Zheng, Yong Li, Zhiwei Liu, Haishuai Wang

Explainability & Interpretability in NLP

Challenge:	Existing document question answering methods reduce inference costs and input tokens.
Approach:	They propose a retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents.
Outcome:	The proposed method surpasses baseline retrieval-augmented generation (RAG) and long-context question answering (LC) methods achieve higher accuracy by processing entire documents, but at the cost of increased computational Corresponding authors.

Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)

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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou

Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)

Challenge:	Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments.
Approach:	They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts.
Outcome:	Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability.

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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)

Challenge:	Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance.
Approach:	They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation.
Outcome:	The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities.

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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

Challenge:	Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis.
Approach:	They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis.
Outcome:	SciAssess evaluates 11 LLMs on multiple tasks across scientific fields.

Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection (2026.acl-long)

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Xiaowei Zhu, Yubing Ren, Fang Fang, Shi Wang, Yanan Cao, Li Guo

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

Challenge:	Existing methods for AI-generated text detection assume uniform token contributions, making them less robust under short sequences or localized token modifications.
Approach:	They propose a training-free method for AI-generated text detection based on an exon-aware token reweighting perspective.
Outcome:	The proposed method achieves state-of-the-art detection performance and robustness to adversarial attacks and varying input lengths.

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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu

Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation (2025.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach:	They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
Outcome:	The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system.

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Dingwei Chen, Ziqiang Liu, Feiteng Fang, Chak Tou Leong, Shiwen Ni, Ahmadreza Argha, Hamid Alinejad-Rokny, Min Yang, Chengming Li

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)

Challenge:	Existing approaches to generating factually inconsistent outputs are resource-intensive.
Approach:	They propose a plug-and-play intervention designed to enhance factuality by inserting premature layers formed through mathematical interpolation with adjacent layers.
Outcome:	The proposed intervention reduces hallucinations while outperforming baselines on four datasets.

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Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, Dacheng Tao

Revisiting Catastrophic Forgetting in Large Language Model Tuning (2024.findings-emnlp)

Challenge:	Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction.
Approach:	They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model.
Outcome:	The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers.

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Hongyu Li, Liang Ding, Meng Fang, Dacheng Tao

A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing (2022.coling-1)

Challenge:	Catastrophic Forgetting (CF) compromises the effectiveness of large language models during fine-tuning, yet the underlying causes of CF remain largely unexplored.
Approach:	They propose a method to flatten the model loss landscape to mitigate CF by flattening the loss landscape.
Outcome:	The proposed method complements existing anti-forgetting strategies, further enhancing the resistance of LLMs to CF.

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Yaxin Fan, Peifeng Li, Fang Kong, Qiaoming Zhu

D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)

Challenge:	Existing studies have focused on graph-based and transition-based discourse parsing, but no study has investigated the advantages of both paradigms for conversational discourse paring.
Approach:	They propose a distance-aware multi-task framework that incorporates the strengths of transition-based paradigms to facilitate conversational discourse parsing.
Outcome:	The proposed framework improves the graph-based paradigm on long-distance dependency links.

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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan

Challenge:	Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction.
Approach:	They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process.
Outcome:	The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process .

DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack (2026.findings-acl)

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Hao Li, Yubing Ren, Yanan Cao, Yingjie Li, Fang Fang, Shi Wang, Li Guo

ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis (2026.acl-long)

Challenge:	Existing watermarking algorithms focus on defending against paraphrase and piggyback spoofing attacks, which can inject harmful content, compromise reliability, and undermine trust in attribution.
Approach:	They propose an algorithm capable of defending against paraphrase and spoofing attacks.
Outcome:	Experiments on large language models and language models show that DualGuard is the first watermarking algorithm capable of defending against both paraphrase and spoofing attacks.

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Runkai Li, Jia Xiong, Xiuyuan He, Jieru Zhao, Jiaqi Lv, Haowen Fang, Lei Qi, Xi Wang

Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework (2026.findings-acl)

Challenge:	High-Level Synthesis (HLS) is a hardware design tool that can be used to design hardware from C-like languages, but its widespread adoption is limited by strict coding constraints and design-specific optimizations.
Approach:	They propose a multi-agent HLS design framework that leverages specialized LLMs for automated debugging and directive tuning.
Outcome:	The proposed framework outperforms Gemini-3-pro in debugging and speedups across various HLS kernels and neural network accelerators.

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Zhuoshang Wang, Yubing Ren, Yanan Cao, Fang Fang, Xiaoxue Li, Li Guo

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Existing secret-key schemes tightly couple detection with injection . this dependency creates a fundamental barrier for real-world governance .
Approach:	et al. introduce a black-box framework for non-intrusive, third-party watermark verification . they propose a proxy model to amplify watermark-relevant signals and complementary relative measurements .
Outcome:	a new framework decouples detection from injection and assesses alignment of query text with watermark distributions.

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Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian

Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)

Challenge:	Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic.
Approach:	They propose a chain-of-thought reasoning framework with three key designs to address these issues.
Outcome:	The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG.

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Ziqian Zeng, Weimin Ni, Tianqing Fang, Xiang Li, Xinran Zhao, Yangqiu Song

Low-Resource NLP

Challenge:	Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive.
Approach:	They propose to query a masked language model with cloze style prompts to obtain supervision signals.
Outcome:	The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%.

AIDE: Attribute-Guided MultI-Hop Data Expansion for Data Scarcity in Task-Specific Fine-tuning (2025.acl-industry)

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Jiayu Li, Jennifer Zhu, Fang Liu, Yanjun Qi

MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)

Challenge:	Existing methods for fine-tuning large language models for specific tasks require extensive seed datasets or struggle to balance task relevance and data diversity.
Approach:	They propose a data synthesis framework that uses a multi-hop process to expand very few seed data points while ensuring data diversity and task relevance.
Outcome:	The proposed framework outperforms state-of-the-art methods in task-specific fine-tuning by over 30%.

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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

GASE: Graph-Aware Semantic Embedding Learning with Frozen LLMs for Text-Attributed Graphs (2026.acl-long)

Challenge:	Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs.
Approach:	They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair.
Outcome:	The proposed framework integrates graphical information of two molecules in pair.

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Mingqian Ding, Jianjun Li, Wenqi Yang, Zhibo Zhang, Yushen Fang

CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models (2023.acl-long)

Challenge:	Large Language Models (LLMs) have shown strong potential for text-attributed graph (TAG) learning, yet effectively integrating LLM semantics with graph structural information remains challenging.
Approach:	They propose a framework for learning Graph-Aware Semantic Embeddings using frozen LLMs.
Outcome:	The proposed framework outperforms state-of-the-art methods on node classification and achieves a 5 speedup over fine-tuning-based methods.

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Jiaxu Zhao, Meng Fang, Zijing Shi, Yitong Li, Ling Chen, Mykola Pechenizkiy

Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

Challenge:	Existing studies on social biases in language models have focused on only English.
Approach:	They propose to use a Chinese dataset for bias evaluation and mitigation of Chinese conversational language models.
Outcome:	The proposed dataset includes under-explored bias categories, such as ageism and appearance biases, which received less attention in previous studies.

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Zirui Song, Bin Yan, Yuhan Liu, Miao Fang, Mingzhe Li, Rui Yan, Xiuying Chen

Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)

Challenge:	specialized LLMs are often limited in domain-specific applications that require specialized knowledge.
Approach:	They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge.
Outcome:	The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization.

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Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi

TWT: Table with Written Text for Controlled Data-to-Text Generation (2021.findings-emnlp)

Challenge:	Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts.
Approach:	They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models.
Outcome:	The proposed framework can be used to better characterize heterophilic graphs, where neighboring nodes often exhibit different labels.

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Tongliang Li, Lei Fang, Jian-Guang Lou, Zhoujun Li

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Existing methods output hallucinated text that is not faithful on TWT.
Approach:	They propose to generate text conditioned on the structured data and a prefix by leveraging pre-trained neural models.
Outcome:	The proposed approach outperforms state-of-the-art methods under automatic and human evaluation metrics.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)

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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu

SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models (2026.findings-acl)

Challenge:	RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts.
Approach:	They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Outcome:	The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness.

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Shuaimin Li, Liyang Fan, Zeyang li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, Min Yang

Controlled Text Generation Using Dictionary Prior in Variational Autoencoders (2022.findings-acl)

Challenge:	Existing methods for evaluating code large language models assume access to proprietary training corpora or use external reference sets with manually tuned, non-generalizable thresholds.
Approach:	They propose a framework for self-referential leakage detection for gray-box and black-box settings.
Outcome:	The proposed framework improves average F1 by 21.52 points in the gray-box setting and 14.46 points in black-box settings over strong baselines.

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Xianghong Fang, Jian Li, Lifeng Shang, Xin Jiang, Qun Liu, Dit-Yan Yeung

TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making (2025.emnlp-main)

Challenge:	Variational autoencoders (VAEs) have been widely applied in text generation tasks, but they suffer from insufficient representation capacity and poor controllability.
Approach:	They propose a data-driven prior that has expressivity and controllability.
Outcome:	The proposed prior enjoys expressivity and controllability and can be used in language modeling and controlled text generation.

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Kechen Jiao, Zhirui Fang, Jiahao Liu, Bei Li, Qifan Wang, Xinyu Liu, Junhao Ruan, Zhongjian Qiao, Yifan Zhu, Yaxin Xu, Jingang Wang, Xiu Li

Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)

Challenge:	Existing post-SFT methods for embodied AI are constrained by sparse rewards and action-only optimization, resulting in low sample efficiency, poor consistency, and model degradation.
Approach:	They propose to integrate Thought-Centric Preference Optimization (TCPO) into embodied decision-making by transforming sparse reward signals into richer step sample pairs.
Outcome:	The proposed approach achieves an average success rate of 26.67% in the ALFWorld environment, and a 6% improvement over RL4VLM.

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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao

Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs (2025.coling-main)

Challenge:	Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks.
Approach:	They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison.
Outcome:	The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research.

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Yi Fang, Moxin Li, Wenjie Wang, Lin Hui, Fuli Feng

LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models (2025.findings-naacl)

Challenge:	Existing solutions to alleviate hallucination have considered utilizing LLMs’ inherent reasoning abilities to alleviating hallucinism, such as self-correction and diverse sampling methods.
Approach:	They propose a counterfactual multi-agent debate framework that predetermines LLMs' stances to override their inherent biases for answer inspection.
Outcome:	Extensive experiments on four datasets of three tasks demonstrate the superiority of the proposed framework over existing methods.

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Zhaoguang Long, Yuhao Zhou, Shangqing Zhao, Yupei Ren, Li Cai, Chenghao Jia, Zhe Chen, Zhe Fang, Yuxiang Song, Man Lan

Forget the Token and Pixel: Rethinking Gradient Ascent for Concept Unlearning in Multimodal Generative Models (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are expanding in scale and size, increasing computational costs . large-scale data compression techniques can reduce the size of training datasets while maintaining data integrity.
Approach:	They propose a large-scale data compression method to reduce the size of training data . they use a bifurcated quantization strategy to maximize the diversity of samples .
Outcome:	The proposed method significantly reduces the size of training data while maximizing the submodular gain.

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Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Hui Zhang, Yongrui Chen, Qianshan Wei, Junfeng Fang, Ruipeng Wang, Sheng Bi, Guilin Qi

Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory (2025.acl-long)

Challenge:	Gradient Ascent (GA) has emerged as a promising approach for concept unlearning in Multimodal Generative Models (MGMs).
Approach:	They propose a novel approach that selectively applies GA to targeted Conceptual Knowledge while preserving Natural Knowledge through Gradient Descent (GD).
Outcome:	The proposed approach removes Conceptual Knowledge and inadvertently diminishes Natural Knowledge, resulting in utility degradation.

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Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan

Dual-Reasoner: Bridging Interleaved Atomicity and Streaming Latency via Thinking-while-Talking (2026.findings-acl)

Challenge:	Recent studies have shown that scaling test-time compute can also effectively improve reasoning.
Approach:	They propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times.
Outcome:	The proposed method significantly improves the scaling performance of majority voting on large language models.

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Yangzhuo Li, Shengpeng Ji, Yifu Chen, Tianle Liang, Haoyu Yang, null Junboli, Jun Fang, Lin Li, Qingyang Hong

Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)

Challenge:	Existing methods to integrate Chain-of-Thought into spoken dialogue models incur prohibitive latency.
Approach:	They propose a Streaming Masking Mechanism to ensure uninterrupted audio streaming . they use a quadruple-constraint system to reconstruct logical atomicity .
Outcome:	Experimental results show that Dual-Reasoner improves speech generation performance with low latency.

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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li

Green & Sustainable NLP

Challenge:	Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization .
Approach:	They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume.
Outcome:	Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency.

MPL: Multiple Programming Languages with Large Language Models for Information Extraction (2025.findings-acl)

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Bo Li, Gexiang Fang, Wei Ye, Zhenghua Xu, Jinglei Zhang, Hao Cheng, Shikun Zhang

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Existing research focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs during the supervised fine-tuning phase.
Approach:	They propose a framework that incorporates programming languages into IE tasks . they introduce function-prompt with virtual running to simulate code-style inputs .
Outcome:	The proposed framework exploits the potential of different programming languages during the supervised fine-tuning phase.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction (2026.acl-long)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He

Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)

Challenge:	End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored.
Approach:	They propose an open-source benchmark to evaluate spoken dialogue systems under natural multi-turn interaction patterns.
Outcome:	The proposed model fails on the highest-performing model with 54.65% pass rate.

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Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, Jiajun Chen

Cross-Lingual Transfer

Challenge:	Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints.
Approach:	They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media.
Outcome:	The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method.

Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)

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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang

Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)

Challenge:	Foundational models and their checkpoints have advanced deep learning, boosting performance across applications.
Approach:	They propose a method for pruning fine-tuned models by calculating differences between them and original model.
Outcome:	The proposed method can improve performance across vision, NLP, and multi-modal benchmarks.

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Xuyang Wu, Shuowei Li, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang

MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)

Challenge:	Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors.
Approach:	They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes.
Outcome:	The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages.

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Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li

T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation (2026.findings-acl)

Challenge:	Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams.
Approach:	They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids .
Outcome:	The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct .

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Kaiyue Sun, Rongyao Fang, Chengqi Duan, Xian Liu, Aoxue Li, Xihui Liu

Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering (2024.findings-acl)

Challenge:	Text-to-image (T2I) generative models have demonstrated exceptional capability in synthesizing high-quality images from textual prompts.
Approach:	They propose a benchmark to explore the knowledge-driven reasoning capabilities of T2I models.
Outcome:	The proposed benchmark examines the knowledge-driven reasoning capabilities of T2I models.

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Yichi Zhang, Zhuo Chen, Yin Fang, Yanxi Lu, Li Fangming, Wen Zhang, Huajun Chen

Natural Language Interfaces

Challenge:	Domain-specific question answering (QA) requires a comprehensive understanding of a specific domain to answer specialized questions.
Approach:	They propose a new alignment objective to align the LLM preference with different human preferences uniformly to optimize LLM performance in real-world, domain-specific QA settings.
Outcome:	The proposed pipeline is superior for real-scenario domain-specific question answering with LLMs.

UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)

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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou

Challenge:	Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems.
Approach:	They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets.
Outcome:	The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization.

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering (2023.findings-acl)

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

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Yung-Sung Chuang, Wei Fang, Shang-Wen Li, Wen-tau Yih, James Glass

CascadeFix: Multi-Location Program Repair via Cascading Planning and Generation (2026.findings-acl)

Challenge:	Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points . dense retrievers are limited by their inability to perform semantic matching for relevant passages that have low lexical overlap with the query.
Approach:	They propose a query expansion and reranking approach for improving passage retrieval with the application to open-domain question answering.
Outcome:	Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points when compared to a vanilla query expansion model and a dense retrieval model.

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Huan Zhang, Li Kuang, Yang Yang, Yilei Fang, Yingjie Xia

Text Clustering

Challenge:	Existing methods for automating program repair face insufficient bug dependency modeling and inadequate global repair planning when addressing semantically complex multi-location bugs.
Approach:	They propose a multi-location automatic repair method via cascading planning and generation . they propose to model dependencies among bugs and cluster them to ensure rationality .
Outcome:	The proposed method resolves 84 multi-location bugs, achieving a 31% improvement over current methods.

FocusLLM: Precise Understanding of Long Context by Dynamic Condensing (2025.acl-long)

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Zhenyu Li, Yike Zhang, Tengyu Pan, Yutao Sun, Zhichao Duan, Junjie Fang, Rong Han, Zixuan Wang, Jianyong Wang

Challenge:	Existing context condensing methods cannot accurately understand the full context, as there is a considerable amount of information loss in the condensed process.
Approach:	They propose a framework to extend the fixed context length of any decoder-only LLM by distilling crucial information from long sequences.
Outcome:	The proposed framework extends the fixed context length of any decoder-only LLM, allowing it to focus on relevant information from very long sequences.

OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces (2026.findings-acl)

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Xiaozhe Li, Jixuan Chen, Xinyu Fang, Shengyuan Ding, Haodong Duan, Qingwen Liu, Kai Chen

KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and tool use, but their ability to continuously refine solutions in response to dynamic environmental feedback remains underexplored.
Approach:	They propose a benchmark to evaluate self-improvement capabilities in large-scale search spaces by combining 20 machine learning tasks with 10 classic NP-hard problems.
Outcome:	The proposed framework emulates human-like cognitive adaptation and operates via a general perception–memory–reasoning loop, iteratively refining solutions based on environmental feedback.

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Xian Peng, Pan Yuan, Dong Li, Junlong Cheng, Qin Fang, Zhi Liu

Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)

Challenge:	Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings.
Approach:	They propose a multi-agent framework for structured Socratic teaching with LLMs that integrates a structured SocRule and a consultant-teacher collaborative teaching mechanism.
Outcome:	The proposed framework outperforms existing LLMs in natural language generation and dialogue comprehension in the classroom.

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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin

InsertGNN: A Hierarchical Graph Neural Network for the TOEFL Sentence Insertion Problem (2024.findings-emnlp)

Challenge:	Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding.
Approach:	They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications.
Outcome:	The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows.

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Fang Wu, Stan Li

Structured Data in NLP

Challenge:	Existing methods that focus on sentence arrangement, textual consistency, and question answering have been shown to be inadequate in addressing this issue.
Approach:	They propose a method which conceptualizes the problem as a graph and employs a hierarchical Graph Neural Network (GNN) to comprehend the interplay between sentences.
Outcome:	The proposed approach outperforms existing methods on a TOEFL dataset and on the arXiv dataset.

MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational Agents (2025.findings-naacl)

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Wanqi Yang, Yanda Li, Meng Fang, Ling Chen

Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs (2024.emnlp-main)

Challenge:	Existing time-aware datasets that focus on persona-grounded conversations focus on temporal dynamics, which narrows their scope and diminishes their complexity.
Approach:	They propose a multimodal, time-aware persona dialogue dataset that integrates linguistic, visual, and temporal elements within dialogue and persona memory.
Outcome:	The proposed framework integrates linguistic, visual, and temporal elements within dialogue and persona memory to assess a model’s ability to understand implicit temporal cues and dynamic interactions.

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Liu Ran, Zhongzhou Liu, Xiaoli Li, Yuan Fang

Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)

Challenge:	Existing methods to predict instances for missing relations on knowledge graphs are limited by their limited training examples.
Approach:	They propose a context-aware adapter for few-shot relation learning in KGs . they propose tunable relation adaptation and contextual information for each relation .
Outcome:	Experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods.

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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi

Tokenization Falling Short: On Subword Robustness in Large Language Models (2024.findings-emnlp)

Challenge:	Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable.
Approach:	This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning .
Outcome:	The proposed study examines the safety and security risks of large reasoning models.

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Yekun Chai, Yewei Fang, Qiwei Peng, Xuhong Li

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)

Challenge:	Language models typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary.
Approach:	They propose to tokenize raw text into sequences of subword identifiers from a predefined vocabulary . they also investigate the challenges and their impact on large language models .
Outcome:	The proposed model can mitigate tokenization issues, but still suffer from typos and other variations.

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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun

RETAIN: Interactive Tool for Regression Testing Guided LLM Migration (2024.emnlp-demo)

Challenge:	Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks.
Approach:	They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies.
Outcome:	The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%.

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Tanay Dixit, Daniel Lee, Sally Fang, Sai Sree Harsha, Anirudh Sureshan, Akash Maharaj, Yunyao Li

Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly integrated into diverse applications.
Approach:	They propose a tool specifically designed for regression testing during LLM migrations.
Outcome:	RETAIN (REgression Testing guided LLM migrAtIoN) provides a tool specifically designed for regression testing during LLM migrations.

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Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao

Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)

Challenge:	Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody.
Approach:	They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content.
Outcome:	The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm.

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Linyu Fan, Wu Wu Yiheng, Jun Xie, Junhui Li, Fang Kong, Guodong Zhou

Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

Challenge:	Recent studies on AMR parsing often regard this task as a seq2seq translation problem.
Approach:	They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding.
Outcome:	The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0.

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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.acl-long)

Challenge:	MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach:	They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome:	The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong

Not All Voices Are Rewarded Equally: Probing and Repairing Reward Models across Human Diversity (2025.findings-emnlp)

Challenge:	Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data.
Approach:	They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process.
Outcome:	Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities.

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Zihao Li, Feihao Fang, Xitong Zhang, Jiaru Zou, Zhining Liu, Wei Xiong, Ziwei Wu, Baoyu Jing, Jingrui He

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization (2025.findings-emnlp)

Challenge:	Using real-world datasets, we conduct the most comprehensive study to date, auditing various state-of-the-art reward models across nine sensitive attributes, including age, gender, ethnicity, etc.
Approach:	They propose a method to mitigate group disparities in reward modeling by using real-world data.
Outcome:	The proposed method is based on a population-based dataset with nine demographic attributes, including gender, ethnicity, age, gender, and ethnicity.

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Jing Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li

ATLAS: Agent Tuning via Learning Critical Steps (2025.findings-acl)

Challenge:	Comparative Policy Optimization (CPO) redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise score.
Approach:	They propose a method to optimize subjective tasks by shifting from sample-wise to comparative group-wise scoring.
Outcome:	The proposed framework shifts from sample-wise scoring to comparative group-wise score . it minimizes contextual bias and enables more robust and fair performance evaluation.

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Zhixun Chen, Ming Li, Yuxuan Huang, Yali Du, Meng Fang, Tianyi Zhou

Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding (2025.emnlp-main)

Challenge:	Existing agent tuning approaches employ supervised finetuning on entire expert trajectories, but behavior-cloning of full traitories introduces expert bias and weakens generalization to states not covered by the expert data.
Approach:	They propose a method that finetunes LLMs on critical steps in expert trajectories and identifies and finetuns them on these steps with reduced costs.
Outcome:	The proposed method outperforms existing methods and open-source LLM agents on only 30% critical steps in extensive experiments.

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Congchi Yin, Qian Yu, Zhiwei Fang, Changping Peng, Piji Li

Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)

Challenge:	Recent studies have successfully decoded natural language from non-invasive brain signals . current dataset splitting methods suffer from data leakage problem .
Approach:	They propose a right cross-subject data splitting criterion without data leakage for decoding fMRI and EEG signal to text.
Outcome:	The proposed method overfits and overestimates brain-to-text decoding models.

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Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, Guodong Zhou

Challenge:	Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives .
Approach:	They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations.
Outcome:	The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus.

Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)

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Tianqiao Liu, Qiang Fang, Wenbiao Ding, Hang Li, Zhongqin Wu, Zitao Liu

Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)

Challenge:	Existing models for generating mathematical word problems are lacking in educational assessment.
Approach:	They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations.
Outcome:	The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance.

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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang

Structured Data in NLP

Challenge:	Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks.
Approach:	They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration.
Outcome:	The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks.

Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)

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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun

Think before Go: Hierarchical Reasoning for Image-goal Navigation (2026.acl-long)

Challenge:	Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities.
Approach:	They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts.
Outcome:	The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs.

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Pengna Li, Kangyi Wu, Shaoqing Xu, Fang Li, Lin Zhao, Long Chen, Zhi-Xin Yang, Nanning Zheng

DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)

Challenge:	Existing methods for image-goal navigation fail to extract informative visual cues, leading agents to wander around.
Approach:	They propose a framework that decomposes image-goal navigation into high-level planning and low-level execution.
Outcome:	The proposed method is superior to existing methods in both simulation and real-world environments.

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Yubing Ren, Yanan Cao, Hao Li, Yingjie Li, Zixuan ZM Ma, Fang Fang, Ping Guo, Wei Ma

Implicit Deep Latent Variable Models for Text Generation (D19-1)

Challenge:	Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model .
Approach:	They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments.
Outcome:	The proposed dataset has over 56,000+ events and 242,000+ arguments.

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Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, Changyou Chen

AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)

Challenge:	Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons.
Approach:	They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior .
Outcome:	The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue.

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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian

Challenge:	Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs).
Approach:	They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say .
Outcome:	The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels.

AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)

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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang

Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models (2025.findings-acl)

Challenge:	Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns.
Approach:	They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback.
Outcome:	The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy.

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Wanqi Yang, Yanda Li, Meng Fang, Yunchao Wei, Ling Chen

Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training (2022.findings-acl)

Challenge:	Existing research focused on model-specific adversarial methods, but real-world applications demand a more generalizable approach to audio adversarials.
Approach:	They propose a Chat-Audio Attacks benchmark to evaluate LALMs' robustness . they propose standard evaluation, GPT-4o-based evaluation and human evaluation .
Outcome:	The proposed benchmark aims to explore the robustness of six state-of-the-art LALMs with voice interaction capabilities.

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Peixin Huang, Xiang Zhao, Minghao Hu, Yang Fang, Xinyi Li, Weidong Xiao

Named Entity Recognition

Challenge:	Existing studies treat named entity recognition as a sequential labeling problem.
Approach:	They propose a span selection framework for nested named entity recognition . they propose nesting entities with different input categories would be separately extracted .
Outcome:	The proposed framework outperforms competing models on four benchmark datasets.

Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)

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Zhisong Zhang, Yan Wang, Xinting Huang, Tianqing Fang, Hongming Zhang, Chenlong Deng, Shuaiyi Li, Dong Yu

Joint Pre-Encoding Representation and Structure Embedding for Efficient and Low-Resource Knowledge Graph Completion (2024.emnlp-main)

Challenge:	Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling.
Approach:	They propose to use parallel context encoding to reduce attention entropy by incorporating attention sinks and selective mechanisms to reduce irregular attention . they also propose to incorporate attention sink mechanisms into the parallel encoded context to reduce the irregular attention.
Outcome:	The proposed methods lower irregular attention entropy and narrow performance gaps.

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Chenyu Qiu, Pengjiang Qian, Chuang Wang, Jian Yao, Li Liu, Fang Wei, Eddie Eddie

Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)

Challenge:	Existing knowledge graph completion models require longer training and inference times as well as increased memory usage.
Approach:	They propose to encode textual descriptions into semantic representations before training and integrate structural embedding with pre-encoded semantic description to improve model's prediction performance on 1-N relations.
Outcome:	The proposed model increases inference speed by 30x and reduces training memory by approximately 60% on the WN18RR and UMLS datasets.

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Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang

To See a World in a Spark of Neuron: Disentangling Multi-Task Interference for Training-Free Model Merging (2025.emnlp-main)

Challenge:	Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks.
Approach:	They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm.
Outcome:	The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios.

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Zitao Fang, Guodong Du, Shuyang Yu, Yifei Guo, Yiwei Zhang, Yiyao Cao, Jing Li, Ho-Kin Tang, Sim Kuan Goh

UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces (2025.acl-long)

Challenge:	Existing approaches to model merging ignore the fundamental roles of neurons, connectivity and activation.
Approach:	They propose a framework that relies on neuronal mechanisms to mitigate task interference . they decomposed task-specific representations into two complementary subspaces . their results offer new insights into mitigating task interference and improving knowledge fusion .
Outcome:	The proposed framework reduces task interference within neurons and improves knowledge fusion.

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Baining Zhao, Jianjie Fang, Zichao Dai, Ziyou Wang, Jirong Zha, Weichen Zhang, Chen Gao, Yue Wang, Jinqiang Cui, Xinlei Chen, Yong Li

AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark (2025.findings-emnlp)

Challenge:	Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored.
Approach:	They propose a benchmark to evaluate whether large multimodal models can process continuous first-person visual observations like humans.
Outcome:	The proposed model can process first-person visual observations like humans, enabling recall, perception, reasoning, and navigation.

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Lan Li, Liri Fang, Bertram Ludäscher, Vetle I Torvik

Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)

Challenge:	Data cleaning is a time-consuming and error-prone manual process even with modern workflow tools like OpenRefine.
Approach:	AutoDCWorkflow generates a table with a data analysis purpose and generates an open-refine workflow.
Outcome:	The proposed pipeline generates clean, minimal tables for data analysis tasks.

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Hao Li, Yanan Cao, Yubing Ren, Fang Fang, Lanxue Zhang, Yingjie Li, Shi Wang

Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs (2026.findings-acl)

Challenge:	Existing models do not build dependency information among event argument roles . Existing methods do not learn the interactions between different roles based on event structure .
Approach:	They propose an intra-event and inter-e event dependency-aware graph network to model dependencies between roles . they use event structure as the fundamental unit to construct role dependencies within events .
Outcome:	The proposed model improves on the ACE05, RAMS, and WikiEvents datasets.

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Xiaozhe Li, Xinyu Fang, Shengyuan Ding, Yang Li, Linyang Li, Haodong Duan, Qingwen Liu, Kai Chen

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

Challenge:	Existing benchmarks focus on correctness, overlooking optimality . large language models excel at math, coding, logic and puzzles .
Approach:	They propose a framework for training and evaluating Large Language Models on NP-hard optimization problems through quality-aware RLVR.
Outcome:	The proposed framework outperforms existing benchmarks on math, coding, logic and puzzles.

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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

Green & Sustainable NLP

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.

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning (2026.acl-long)

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Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang, Wei Ye

Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding (2026.acl-long)

Challenge:	Existing models that ground retrieval on external evidence are limited in their ability to implement retrieval-augmented generation.
Approach:	They propose a retrieval-augmented generation model that embeds retrieval control directly into generation.
Outcome:	The proposed model surpasses strong RAG baselines and uses substantially fewer parameters.

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Wenkai Wang, Xiyun Li, Hongcan Guo, Wenhao Yu, Tianqing Fang, Haitao Mi, Dong Yu, Shengyu Zhang

How Large Language Models Balance Internal Knowledge with User and Document Assertions (2026.findings-acl)

Challenge:	Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates due to visually homogeneous elements and dense layouts.
Approach:	They propose to replace static consistency strategies with a learnable selection mechanism that selects the optimal target by critiquing its own proposals rendered on the screenshot.
Outcome:	The proposed model significantly improves both grounding and critiquing capabilities over 6 benchmarks.

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Shuowei Li, Haoxin Li, Wenda Chu, Yi Fang

Teams of LLM Agents can Exploit Zero-Day Vulnerabilities (2026.eacl-long)

Challenge:	Large language models often need to balance their internal parametric knowledge with external information, such as user beliefs and content from retrieved documents, in real-world scenarios like RAG or chat-based systems.
Approach:	They propose a three-source interaction framework to evaluate 27 large language models from 3 families on 2 datasets.
Outcome:	The proposed framework systematically evaluates 27 large language models from 3 families on 2 datasets.

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Yuxuan Zhu, Antony Kellermann, Akul Gupta, Philip Li, Richard Fang, Rohan Bindu, Daniel Kang

Challenge:	Existing frameworks for LLM agents fail to exploit real-world zero-day vulnerabilities . prior work has shown that simple agents can hack mock "capture-the-flag" websites .
Approach:	They propose a system of agents with a planning agent that can launch subagents to exploit real-world vulnerabilities.
Outcome:	The proposed framework improves over previous frameworks by up to 4.3 on 14 real-world vulnerabilities.

MECH: A Cost-Effective Multi-Task Cascade Framework for Classroom Opinion Evolution Recognition (2026.acl-long)

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Yancui Li, Xiaoyu Zhou, Guoyi Miao, Fang Kong

Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition (2023.emnlp-main)

Challenge:	Existing studies focus on dialogue act annotation, overlooking the deeper dimension of opinion evolution.
Approach:	They propose a framework for Classroom Opinion Evolution Recognition that translates "Action-Opinion" dualism into a risk-aware routing mechanism.
Outcome:	The proposed framework achieves state-of-the-art accuracy of 78.55% while reducing API costs by 44.4%.

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Yaxin Fan, Feng Jiang, Peifeng Li, Fang Kong, Qiaoming Zhu

CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning (2026.findings-acl)

Challenge:	Existing approaches to learn dialogue discourse parsing with related tasks require additional annotation, thus limiting their generality.
Approach:	They propose a multitasking framework that integrates dialogue discourse parsing with addressee recognition to reflect relation-based structure of dialogue.
Outcome:	The proposed framework outperforms baselines on the Molweni and STAC datasets.

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Lingling Shi, Haoyu Jin, Ruiyu Fang, Shuangyong Song, Jinsong Su, Yongxiang Li, Xuelong Li

Machine Translation

Challenge:	Large Language Models suffer from paraphrasing errors, omissions, or hallucinations when input contains translation-specific elements that require strict preservation or controlled transformation.
Approach:	They propose a Controllable Element-Oriented Machine Translation framework that decomposes the translation process into a linguistically grounded analysis, strategy formulation, and final generation.
Outcome:	The proposed framework improves on the WMT23/24 Chinese–English benchmarks while significantly reducing element-level constraint violations.

ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization (2024.lrec-main)

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Mengsha Liu, Daoyuan Chen, Yaliang Li, Guian Fang, Ying Shen

Visual Data in NLP

Challenge:	Existing methods for chart summarization lack visual-language matching and reasoning ability.
Approach:	They propose a method which synthesizes deep analysis based on chains of thought and strategies of context retrieval to improve the logical coherence and accuracy of the generated summaries.
Outcome:	The proposed method outperforms 8 state-of-the-art models over 7 evaluation metrics and can significantly reduce time and cognitive resources required.

EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)

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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen

FARSS: Fisher-Optimized Adaptive Low-Rank and Singular-Vector Selection for Knowledge-Preserving Fine-Tuning (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community .
Approach:	They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction.
Outcome:	The proposed framework is open-source and available on Github.

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Renxing Chen, Ziwei Xiang, Peisong Wang, Hongjian Fang, Meng Li, Fanhu Zeng, Yanan Zhu, Peipei Yang, Xu-Yao Zhang, Jian Cheng

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

Challenge:	Low-rank adaptation methods for large language models have limitations in preserving world knowledge and limiting updates to preserve world knowledge.
Approach:	They propose a Fisher-optimized adaptive low Rank and Singular-VectorSelection framework for knowledge-preserving fine-tuning that allows efficient and task-sensitive updates.
Outcome:	The proposed framework outperforms existing methods for knowledge-preserving fine-tuning.

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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song

Green & Sustainable NLP

Challenge:	Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability.
Approach:	They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones.
Outcome:	The proposed framework shows that it is robust to different prompts and superior to previous methods.

Reverse Preference Optimization for Complex Instruction Following (2025.findings-acl)

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Xiang Huang, Ting-En Lin, Feiteng Fang, Yuchuan Wu, Hangyu Li, Yuzhong Qu, Fei Huang, Yongbin Li

Collaborative Document Simplification Using Multi-Agent Systems (2025.coling-main)

Challenge:	Existing methods for identifying and evaluating preference pairs with multiple constraints are noisy.
Approach:	They propose a method that dynamically reverses constraints to ensure the chosen response is perfect.
Outcome:	The proposed method reduces noise in preference pairs by reversing constraints to ensure the chosen response is perfect.

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Dengzhao Fang, Jipeng Qiang, Xiaoye Ouyang, Yi Zhu, Yunhao Yuan, Yun Li

U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents (2026.findings-acl)

Challenge:	Document simplification requires complex factors such as technical terminology, metaphors, and overall coherence.
Approach:	They propose a multi-agent framework for document simplification based on large language models that emulates the collaborative process of a human expert team through the roles played by multiple agents.
Outcome:	The proposed framework emulates the collaborative process of a human expert team through the roles played by multiple agents, addressing the intricate demands of document simplification.

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Jin Su, Runnan Fang, Yeqiu Li, Xiaobin Wang, Shihao Cai, Pengjun Xie, Ningyu Zhang, Fajie Yuan

Redundancy Principles for MLLMs Benchmarks (2025.acl-long)

Challenge:	Existing context-folding methods are designed for single-query or single-intent scenarios.
Approach:	They propose a dynamic context-folding framework tailored to user-centric tasks that preserves fine-grained information through dynamic context folding.
Outcome:	The proposed framework outperforms ReAct and previous folding frameworks on long, noisy tasks.

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Zicheng Zhang, Xiangyu Zhao, Xinyu Fang, Chunyi Li, Xiaohong Liu, Xiongkuo Min, Haodong Duan, Kai Chen, Guangtao Zhai

Debiasing LLMs by Masking Unfairness-Driving Attention Heads (2026.findings-acl)

Challenge:	Rapid growth of Multi-modality Large Language Models has led to significant redundancy among benchmarks.
Approach:	They propose a framework to improve MLLM benchmark design by identifying redundancy at three levels: dimension, instance, and cross-benchmark redundancies.
Outcome:	The proposed framework streamlines evaluations and enhances reliability.

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Tingxu Han, Wei Song, Ziqi Ding, Ziming Li, Chunrong Fang, Yuekang Li, Dongfang Liu, Zhenyu Chen, Zhenting Wang

Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents (2025.acl-long)

Challenge:	Existing work probes when biased outputs appear, but gives little insight into the mechanisms that generate them, leaving existing mitigations largely fragile.
Approach:	They propose a lightweight debiasing framework that detects bias heads and selectively masks only those heads that activate under DA and CoT.
Outcome:	The proposed framework reduces unfairness by 391.9%- 534.5% in both one- and two-turn dialogues.

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Fanhang Man, Huandong Wang, Jianjie Fang, Zhaoyi Deng, Baining Zhao, Xinlei Chen, Yong Li

Sentiment Analysis

Challenge:	Existing methods to predict sentiments on social media are limited and do not consider reciprocal influences among social media users.
Approach:	They propose a multi-perspective role-playing framework to simulate human response processes to extract sentiment-related features from social media messages.
Outcome:	The proposed model improves sentiment forecasting at microscopic and macroscopic levels.

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)

Challenge:	Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts.
Approach:	They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce.
Outcome:	The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms.

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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

Natural Language Interfaces

Challenge:	ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets.
Approach:	They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation.
Outcome:	The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models.

When Slower Isn’t Truer: Inverse Scaling Law of Truthfulness in Multimodal Reasoning (2026.findings-acl)

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Sitong Fang, Wenjing Cao, Jiahao Li, Xuyao Wang, Chi-Min Chan, Sirui Han, Juntao Dai, Yike Guo, Yaodong Yang, Jiaming Ji

EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction (2024.findings-emnlp)

Challenge:	a study of slow reasoning models for multimodal reasoning finds that they are more prone to fabricating plausible yet false details when confronted with incomplete or misleading visual inputs.
Approach:	They conduct the first systematic study of the inverse scaling law in slow-thinking paradigms for multimodal reasoning.
Outcome:	The findings suggest that slower reasoning models are more prone to fabricating false details . the study analyzed 5,000-sample hierarchical prompt dataset by 50 participants .

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Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu

UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)

Challenge:	Existing methods to extract product attribute value require multiple extractions to obtain all corresponding values.
Approach:	They propose an Efficient product Attribute Value Extraction approach using lightweight sparse-layer interaction.
Outcome:	The proposed method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large.

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Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li

TinyBERT: Distilling BERT for Natural Language Understanding (2020.findings-emnlp)

Challenge:	Existing methods overlook the challenge of effectively transforming structure information from NL to SQL.
Approach:	They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL.
Outcome:	The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes.

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Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)

Challenge:	Pre-trained language models are computationally expensive and difficult to efficiently execute on resource-restricted devices.
Approach:	They propose a Transformer distillation method that performs Transformer distillations at pre-training and task-specific learning stages.
Outcome:	The proposed method accelerates inference and reduces model size while maintaining accuracy.

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Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering (2024.findings-emnlp)

Challenge:	Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy .
Approach:	They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources.
Outcome:	The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests.

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Wanqi Yang, Yanda Li, Meng Fang, Ling Chen

Natural Language Interfaces

Challenge:	Existing language models have limited sensitivity to temporal information and inadequate temporal reasoning capabilities.
Approach:	They propose a framework that enhances temporal awareness and reasoning . they propose to use Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning .
Outcome:	The proposed framework outperforms existing LLMs on time-sensitive question answering tasks.

INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025.findings-acl)

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Di Wu, Liting Jiang, Bohui Mao, Hongyan Xie, Haoxiang Su, Zhongjiang He, Ruiyu Fang, Shuangyong Song, Hao Huang, Xuelong Li

Sentiment Analysis

Challenge:	Existing studies struggle to achieve performance comparable to that on high-resource languages due to inherent linguistic diversity of multilingual SLU tasks.
Approach:	They propose a multilingual information transfer network to solve these challenges . they propose to reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages.
Outcome:	The proposed model outperforms baseline models on the MASSIVE and MASSIV-UG datasets in overall accuracy across all languages.

Encoding Sentiment Information into Word Vectors for Sentiment Analysis (C18-1)

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Zhe Ye, Fang Li, Timothy Baldwin

TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network (2021.emnlp-main)

Challenge:	Existing methods for embedding sentiment knowledge into word vectors are generally trained independently of the downstream task.
Approach:	They propose to encode sentiment knowledge into pre-trained word vectors to improve sentiment analysis.
Outcome:	The proposed method improves sentiment analysis on four popular sentiment datasets compared to benchmark methods.

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Zheng Fang, Yanan Cao, Tai Li, Ruipeng Jia, Fang Fang, Yanmin Shang, Yuhai Lu

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

Challenge:	Existing methods to label data and identify entities require large amounts of manually annotated texts for training supervised models.
Approach:	They propose a dictionary extension method which extracts new entities through the type expanded model.
Outcome:	The proposed method outperforms state-of-the-art supervised systems on different types of datasets and surpasses supervised models.

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Ziwei Wang, Junjie Zheng, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Fang Zhouhua, Zhiwei Liu, Dajun Chen, Yong Li, Jiajun Bu

Challenge:	Advanced GUI agents suffer from prohibitive deployment costs on resource-constrained devices.
Approach:	They propose a lightweight GUI agent with GUI-specific knowledge and task scalability . LAMO-3B supports monolithic execution and MAS-style orchestration .
Outcome:	The proposed GUI agent LAMO-3B supports monolithic execution and MAS-style orchestration.

MARD: Module-Aware Reasoning Distillation for Language Models with Adaptive Supervision (2026.acl-long)

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Wenqi Yang, Jianjun Li, Zhibo Zhang, Mingqian Ding, Yushen Fang

Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)

Challenge:	Multi-step reasoning remains challenging for language models with limited capacity . et al., 2025) demonstrate remarkable reasoning capabilities across diverse tasks .
Approach:	They propose a module-aware reasoning distillation framework that explicitly targets key Transformer components for effective reasoning transfer.
Outcome:	The proposed framework targets key components for effective reasoning transfer . it adopts an offline distillation setting, where a strong teacher model provides reasoning trajectories in advance .

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Haoyan Liu, Lei Fang, Qian Liu, Bei Chen, Jian-Guang Lou, Zhoujun Li

Programming Languages in NLP

Challenge:	Existing models for text-to-SQL do not explicitly introduce common knowledge to address comparison relations.
Approach:	They propose to leverage adjective-noun phrasing knowledge mined from the web to predict comparison relations in text-to-SQL.
Outcome:	The proposed approach improves on the original and re-split Spider datasets on comparison relation prediction.

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)

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Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song

STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation (2022.acl-long)

Challenge:	Existing approaches to generalize commonsense reasoning lack instantiated knowledge and require pre-built concept taxonomies and annotations.
Approach:	They propose a framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering.
Outcome:	Empirical results show that distilling CANDLE on student models provides benefits across three downstream tasks.

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Qingkai Fang, Rong Ye, Lei Li, Yang Feng, Mingxuan Wang

Phrase-level Textual Adversarial Attack with Label Preservation (2022.findings-naacl)

Challenge:	Existing methods to learn speech representations for end-to-end speech-totext translation (ST) neglect the representation discrepancy across modalities.
Approach:	They propose a method to calibrate the representation discrepancy between modalities by mixing up the representation sequences of different modality inputs.
Outcome:	The proposed method alleviates the cross-modal representation discrepancy and improves on a strong baseline on eight translation directions.

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Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy

PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)

Challenge:	Existing adversarial attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality.
Approach:	They propose a phrase-level perturbation-based adversarial ATtack that generates adversarials through phrase- level perturbations.
Outcome:	The proposed approach improves the performance of natural language processing models by reducing the need for word-level perturbations and preserving the fluency and grammaticality of the samples.

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Yun Luo, Yingjie Li, Xiangkun Hu, Qinglin Qi, Fang Guo, Qipeng Guo, Zheng Zhang, Yue Zhang

CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality (2023.acl-long)

Challenge:	Experimental results show that the main challenge lies in long context and perspective extraction.
Approach:	They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline .
Outcome:	The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform .

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Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li

Structured Data in NLP

Challenge:	Current datasets bias in the English language while leaving other languages underexplored.
Approach:	They propose a Chinese answer-to-sequence dataset with high quality and large scale . they propose encoding space for two hybrid knowledge resources to convert this task to a graph-totext problem.
Outcome:	The proposed method is effective in generating textual descriptions for the Chinese answer-to-sequence dataset.

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang

Challenge:	Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details.
Approach:	They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making.
Outcome:	Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm.

Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)

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Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu

Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)

Challenge:	Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections.
Approach:	They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight.
Outcome:	The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%.

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Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu

Dynamic Evaluation with Cognitive Reasoning for Multi-turn Safety of Large Language Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands.
Approach:	They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans .
Outcome:	The proposed approach reduces human intervention and improves safety and efficiency.

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Lanxue Zhang, Yanan Cao, Yuqiang Xie, Fang Fang, Yangxi Li

CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation (2026.findings-acl)

Challenge:	Existing safety evaluation methods rely on static assessments that use fixed harmful prompts or predefined prefixes as jailbreak templates.
Approach:	They propose a dynamic evaluation framework for multi-turn safety assessment of LLMs based on cognitive theories to simulate real chatting process and scenario simulation and strategy decision to guide dynamic generation.
Outcome:	The proposed framework has been applied to evaluate the safety of widely used LLMs.

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Peiding Wang, Li Zhang, Fang Liu, Chongyang Tao, Yinghao Zhu

Programming Languages in NLP

Challenge:	Existing memory management approaches show promise but remain limited by natural language-centric representations.
Approach:	They propose an AST-guided dynamic memory management system for repository-level iterative code generation that maintains and updates repository context through AST operations.
Outcome:	The proposed system improves instruction following by 12.2% and reduces interaction rounds by 2–3 while maintaining competitive inference latency and token efficiency.

More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation (2024.findings-acl)

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Jiaxu Zhao, Zijing Shi, Yitong Li, Yulong Pei, Ling Chen, Meng Fang, Mykola Pechenizkiy

Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs’ Hallucinations (2026.acl-long)

Challenge:	Existing studies on bias dataset construction and mitigation focus on one demographic group . in real-world applications, there are more than two demographic groups at risk of the same bias.
Approach:	They propose to analyze and reduce biases across multiple demographic groups using a multi-demographic bias dataset.
Outcome:	The proposed method can mitigate biases among multiple demographic groups effectively, the authors show .

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Xinyue Fang, Zhiliang Tian, Zhen Huang, Ziyi Pan, Zhihua Wen, Xi Wang, Quntian Fang, Dongsheng Li