Papers by Hui Li

An Investigation of LLMs’ Inefficacy in Understanding Converse Relations (2023.emnlp-main)

Challenge:	Existing methods to improve performance of large language models rely on additional training objectives or language-specific parameters.
Approach:	They propose a bidirectional language projection framework that enables efficient multilingual alignment and language shift using the intrinsic parameters.
Outcome:	The proposed framework improves performance of non-dominant languages and improves internal representations.

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Chengwen Qi, Bowen Li, Binyuan Hui, Bailin Wang, Jinyang Li, Jinwang Wu, Yuanjun Laili

Challenge:	Existing benchmarks for Large Language Models (LLMs) follow the data distribution of pre-training data.
Approach:	They propose a benchmark ConvRe focusing on converse relations which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets.
Outcome:	The proposed benchmark focuses on converse relations, which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets.

Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning (2021.findings-emnlp)

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Zhan Shi, Hui Liu, Martin Renqiang Min, Christopher Malon, Li Erran Li, Xiaodan Zhu

DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models (2024.findings-acl)

Challenge:	Existing approaches fail to generalize well to concepts that are not observed during training.
Approach:	They propose a framework that revolves around probing several similar image caption training instances and performing analogical reasoning over relevant entities in retrieved prototypes.
Outcome:	The proposed framework improves on the widely used image captioning benchmarks and on composition-related evaluation metrics.

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Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang

CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion (2025.emnlp-main)

Challenge:	Large language models (LLMs) have shown impressive results, but still suffer from hallucination, i.e., the generation of false information.
Approach:	They propose a task of sequential model editing that aims to rectify mistakes continuously.
Outcome:	The proposed method significantly outperforms baselines in single-turn and sequential editing.

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Sheng Zhang, Yifan Ding, Shuquan Lian, Shun Song, Hui Li

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

Challenge:	Recent advances in code large language models have produced repository-level code completion methods that automatically predict the unfinished code based on the broader information from the repository.
Approach:	They propose a framework to identify relevant knowledge for retrieval-augmented repository-level code completion.
Outcome:	The proposed framework significantly outperforms state-of-the-art methods on ReccEval and CCEval.

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

PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit (2022.naacl-demo)

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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Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, Liang Huang

Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection (2020.acl-main)

Challenge:	PaddleSpeech is an open-source speech toolkit that supports speech-to-text and text-to speech tasks.
Approach:	They describe the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to speech tasks.
Outcome:	The proposed framework achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods.

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Yefei Zha, Ruobing Li, Hui Lin

CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)

Challenge:	Off-topic spoken response detection is crucial for an automated speaking assessment system.
Approach:	They propose a novel approach for off-topic spoken response detection with high off-top recall on both seen and unseen prompts.
Outcome:	The proposed model achieves significant improvements in detecting off-topic responses with extremely high on-topic recall on both seen and unseen prompts.

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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin

Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)

Challenge:	Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences.
Approach:	They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks.
Outcome:	The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones.

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Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin, Tianqi Zheng, Dante Everaert, Hanqing Lu, Hui Liu, Hui Liu, Yue Xing, Monica Xiao Cheng, Jiliang Tang

LRMM: Learning to Recommend with Missing Modalities (D18-1)

Challenge:	Existing studies have shown that LLMs struggle to identify the boundaries of their own knowledge and tend to prioritize external information over internal knowledge learned during pre-training.
Approach:	They conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking.
Outcome:	The proposed classifiers improve performance even when dealing with noisy knowledge databases.

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Cheng Wang, Mathias Niepert, Hui Li

Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning (2025.findings-acl)

Challenge:	Existing methods for content-based recommendation with missing or corrupted modalities are lacking in learning multimodal models.
Approach:	They propose a multimodal multimodal autoencoder that learns multimodal representations for complementing and imputing missing modalities.
Outcome:	The proposed framework achieves state-of-the-art performance on rating prediction tasks and is more robust to previous methods in alleviating data-sparsity and the cold-start problem.

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Xinghao Chen, Zhijing Sun, Guo Wenjin, Miaoran Zhang, Yanjun Chen, Yirong Sun, Hui Su, Yijie Pan, Dietrich Klakow, Wenjie Li, Xiaoyu Shen

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

Challenge:	Large Language Models (LLMs) excel in reasoning tasks through Chain-of-Thought prompting.
Approach:	They examine the factors influencing CoT distillation including granularity, format and teacher model.
Outcome:	The proposed model is based on four teacher models and seven student models across seven mathematical and commonsense reasoning datasets.

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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li

Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)

Challenge:	Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion.
Approach:	They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree .
Outcome:	The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work.

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Wei Zhang, Jian Yang, Jiaxi Yang, Ya Wang, Zhoujun Li, Zeyu Cui, Binyuan Hui, Junyang Lin

Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions (2025.acl-long)

Challenge:	Code large language models (LLMs) enhance programming by understanding and generating code across languages.
Approach:	a new benchmark evaluates code understanding and generation in repositories using code large language models.
Outcome:	The proposed model improves code understanding and generation in repositories by evaluating 1,888 test cases across 6 programming languages.

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Hang Li, Tianlong Xu, Kaiqi Yang, Yucheng Chu, Yanling Chen, Yichi Song, Qingsong Wen, Hui Liu

Numerical Reasoning

Challenge:	Recent studies have demonstrated the potential of large language models (LLMs) for automatic error detection in math word problems (MWPs).
Approach:	They propose a framework that generates adaptive reference solutions using LLMs to enhance error detection by reducing conformity bias in MWPs.
Outcome:	The proposed framework mitigates the performance gap between conventional and alternative solutions in MWPs, especially when combined with reasoning-enhancing techniques like chain-of-thought prompting.

KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning (2024.lrec-main)

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Dongyang Li, Taolin Zhang, Longtao Huang, Chengyu Wang, Xiaofeng He, Hui Xue

LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection (2024.naacl-short)

Challenge:	General pre-trained language models (PLMs) leverage relation triples from knowledge graphs (KGs) and integrate external data sources into language models via self-supervised learning.
Approach:	They propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL) to detect positions for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge.
Outcome:	The proposed model can detect essential positions in texts for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge.

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Zhao Zhang, Yiming Li, Jin Zhang, Hui Xu

Sentiment Analysis

Challenge:	Existing methods for stance detection focus on background information and not on the accompanying input texts.
Approach:	They propose to prompt Large Language Models to explicitly extract the relationship between paired text and unseen target as contextual knowledge and inject it into a generation model BART to exploit the rich contexts and semantics.
Outcome:	The proposed model is able to detect stance labels in zero-shot and cross-target scenarios.

NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation (D18-1)

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Xiaoyu Shen, Hui Su, Wenjie Li, Dietrich Klakow

Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026.acl-long)

Challenge:	Sequence-to-Sequence models favor short generic responses . however, the model is not suitable for modeling dialogues .
Approach:	They propose a model that connects preceding and following conversations to a prior distribution to avoid non-differentiability of discrete natural language tokens.
Outcome:	The proposed model is highly efficient in learning the backbone of human-computer communications, but favors short generic responses.

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Yifei Li, Weidong Guo, Lingling Zhang, Rongman Xu, Muye Huang, Hui Liu, Lijiao Xu, Yu Xu, Jun Liu

Challenge:	Existing benchmarks and evaluation protocols focus on surface-level factual recall.
Approach:	They propose a benchmark for assessing cognitive memory under cue–trigger semantic disconnect.
Outcome:	The proposed framework reveals failures not captured by existing benchmarks.

InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)

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Kaixin Li, Qisheng Hu, James Zhao, Hui Chen, Yuxi Xie, Tiedong Liu, Michael Shieh, Junxian He

Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)

Challenge:	InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing.
Approach:	They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing .
Outcome:	The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark .

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Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong

Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)

Challenge:	Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning.
Approach:	They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations.
Outcome:	The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion.

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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu

DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models (2026.acl-long)

Challenge:	Existing methods for AVE are limited on rare attributes due to poor generalization ability.
Approach:	They propose to leverage pretraining and transfer learning to address weaknesses in existing methods.
Outcome:	The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources.

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Xinwei Li, Li Lin, Hui Jiao, Li Yao, Tien-Tsin Wong, Hanqian Wu

A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection (2025.emnlp-main)

Challenge:	Existing decoding-based approaches do not explicitly decouple visual evidence from mixed vision–language representations.
Approach:	They propose to decouple visual evidence from mixed vision–language representations by dynamically identifying layers enriched with visual information and performing intra-layer decoupling to extract aggregated visual evidence.
Outcome:	Experiments show that DiVE achieves state-of-the-art performance on multiple benchmarks.

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Hui Li, Ante Wang, Kunquan Li, Zhihao Wang, Liang Zhang, Delai Qiu, Qingsong Liu, Jinsong Su

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)

Challenge:	Existing methods for misinformation detection are limited by domain knowledge and expert experience.
Approach:	They propose a Multi-Agent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO) they first employ multiple expert agents to analyze target-domain news, then introduce a question-reflection mechanism that guides expert agents for higher-quality analysis.
Outcome:	The proposed framework improves on a common dataset and shows that iteratively improves over existing methods.

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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)

Challenge:	Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection.
Approach:	They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization.
Outcome:	The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models.

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Zefeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li

Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)

Challenge:	Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries .
Approach:	They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries .
Outcome:	The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard.

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Bei Li, Hui Liu, Ziyang Wang, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, Changliang Li

Modularized Interaction Network for Named Entity Recognition (2021.acl-long)

Challenge:	In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence.
Approach:	They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences.
Outcome:	The proposed approach encodes the context and the current sentence without contexts.

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Fei Li, Zheng Wang, Siu Cheung Hui, Lejian Liao, Dandan Song, Jing Xu, Guoxiu He, Meihuizi Jia

Information Extraction & Text Mining

Challenge:	Named Entity Recognition (NER) models focus on word-level information, while segment-based models focus only on word level information.
Approach:	They propose a Modularized Interaction Network (MIN) model which utilizes both word-level information and segment-level dependencies.
Outcome:	The proposed model outperforms the current state-of-the-art models on three NER benchmark datasets.

How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? (2022.findings-acl)

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Hailong Jin, Tiansi Dong, Lei Hou, Juanzi Li, Hui Chen, Zelin Dai, Qu Yincen

Boosting Text-to-SQL through Multi-grained Error Identification (2025.coling-main)

Challenge:	Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer.
Approach:	They propose to transfer semantic knowledge from rich-resourced languages to low-resource languages by using multilingual transfer learning.
Outcome:	The proposed model outperforms baselines and can handle negative transfer.

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Bo Xu, Shufei Li, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, Yanghua Xiao

Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue (2026.findings-acl)

Challenge:	Existing methods for error identification often overlook validation of generated results . text-to-SQL is a technology that converts natural language questions into executable SQL queries .
Approach:	They propose to integrate a multi-grained error identification method into existing methods to detect SQL errors.
Outcome:	The proposed method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities.

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Guangya Yu, Hui Luo, Qi Ye, Ruihui Hou, Weiyan Zhang, Mingxi Shang, Xuanwu Li, ChunMing Wang, Tong Ruan

Contrastive Learning enhanced Author-Style Headline Generation (2022.emnlp-main)

Challenge:	Recent large reasoning models (LLMs) lack dynamic and diverse thinking capabilities . reusing atomic thoughts provides a practical pathway toward dynamic reasoning .
Approach:	They propose a framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses.
Outcome:	The proposed framework extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses.

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Hui Liu, Weidong Guo, Yige Chen, Xiangyang Li

NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval (D18-1)

Challenge:	Current work only uses the article itself in the headline generation, but have not taken the writing style of headlines into account.
Approach:	They propose a model which takes historical headlines into account to integrate the stylistic features of the author into the model and integrate them into the decoder.
Outcome:	The proposed model can integrate the stylistic features of the author into the model and generate a headline that is appropriate for the article and consistent with the author’s style.

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Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, Jungang Xu

Information Retrieval

Challenge:	Existing neural IR models do not have a mechanism for treating expansion terms differently from the original query terms, making it difficult to combine them with existing PRF approaches.
Approach:	They propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks.
Outcome:	Extensive experiments on two standard test collections confirm the effectiveness of the proposed framework in improving the performance of two state-of-the-art neural IR models.

Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts (2024.lrec-main)

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Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, Yongbin Li

Sentiment Analysis

Challenge:	Existing methods for OOD intent detection are limited to single dialogue turns.
Approach:	They propose a context-aware OOD intent detection framework to model multi-turn contexts in OOD context detection tasks using unlabeled data.
Outcome:	The proposed framework improves the F1-OOD score by 29% on multi-turn OOD detection tasks compared to the previous best method.

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment (2026.acl-long)

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Zhanyu Liu, Qingguo Hu, Ante Wang, Chenqing Liu, Zhishang Xiang, Hui Li, Delai Qiu, Jinsong Su

Low-Resource NLP

Challenge:	Existing methods for training reasoning-oriented large language models assume high-resource settings with abundant data.
Approach:	They propose a framework that integrates high-value general-domain data to promote more diverse exploration.
Outcome:	The proposed framework matches or surpasses RLVR trained with 32 target-domain samples using 32 target domain samples.

Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models (2025.findings-acl)

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Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He

Privacy Risks of Intermediate Representations: Attribute Inference in Distributed LLM Inference (2026.findings-acl)

Challenge:	Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps.
Approach:	They propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
Outcome:	The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT.

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Yang Lyu, Jin Cao, Yang Xiao, Zhe Sun, Ben Niu, Fenghua Li, Hui LI

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)

Challenge:	Distributed LLMs avoid raw inputs by transmitting intermediate hidden states, a practice widely assumed to preserve privacy.
Approach:	They propose a distributed inference framework that transmits intermediate hidden states to avoid sending raw inputs by exposing sensitive user attributes.
Outcome:	The proposed approach achieves Top-1 accuracy of 0.997 on CMS, 0.980 on Skytrax, and 0.986 on ECHR.

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Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu

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

Challenge:	Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory.
Approach:	a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities .
Outcome:	Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory .

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

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)

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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Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Zhou Jingbo, Dejing Dou, Hui Xiong

OntoED: Low-resource Event Detection with Ontology Embedding (2021.acl-long)

Challenge:	Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables.
Approach:	They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input.
Outcome:	The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets.

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Shumin Deng, Ningyu Zhang, Luoqiu Li, Chen Hui, Tou Huaixiao, Mosha Chen, Fei Huang, Huajun Chen

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

Challenge:	Existing methods to ED rely on training instances and ignore correlation of event types.
Approach:	They propose a process of event ontology population linking event instances to pre-defined event types in event ontoology and ontological embedding to address these problems.
Outcome:	The proposed framework can be applied to new unseen event types by establishing linkages to existing ones.

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

Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System (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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Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou, Nanqing Dong

STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding (2026.findings-acl)

Challenge:	Recent AI methods have shown promise in tasks such as hypothesis generation and experimental design, but they fail to replicate the collaborative nature of real-world scientific practices.
Approach:	They propose a virtual scientific system that mimics the collaborative nature of scientific research by organizing a team of agents to generate, evaluate, and refine research ideas.
Outcome:	The proposed system outperforms the state-of-the-art method in producing new scientific ideas and offers valuable insights to guide future research.

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Hongwang Xiao, Wenjun Lin, Xi Chen, Hui Wang, Kai Chen, Jiashan Li, Yuancheng Sun, Sicheng Dai, Boya Wu, Qiwei Ye

Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks (2024.naacl-long)

Challenge:	a multimodal protein language model (LLM) integrates sequence, structure, and function into functional annotation.
Approach:	They propose a multimodal protein language model that synergistically aligns bimodal representations with the textual modality to advance protein functional annotation.
Outcome:	The proposed model synergizes bimodal representations with the textual modality to advance protein functional annotation.

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Yunqi Zhang, Songda Li, Chunyuan Deng, Luyi Wang, Hui Zhao

Visual Data in NLP

Challenge:	Existing vision-language models focus on salient attributes but ignore contextualized nuances, resulting in gender bias.
Approach:	They propose a task-agnostic generation framework to mitigate gender bias in vision-language models.
Outcome:	The proposed framework can mitigate gender bias in vision-language models . it yields all-sided but gender-obfuscated narratives, which prevents concentration on localized image features, especially gender attributes.

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

START: Self-taught Reasoner with Tools (2025.emnlp-main)

Challenge:	Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon .
Approach:	They propose a routing-guided intervention method that enhances domain expert activation.
Outcome:	The proposed method achieves consistent improvements on visual reasoning tasks.

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Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang, Beichen Zhang, Bowen Yu, Binyuan Hui, Junyang Lin, Xiang Wang, Dayiheng Liu

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2026.acl-long)

Challenge:	Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations.
Approach:	They propose a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints and a framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis.
Outcome:	Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%).

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Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, Yafeng Deng

Are Large Language Models (LLMs) Good Social Predictors? (2024.findings-emnlp)

Challenge:	Existing memory systems for LLMs store isolated records and retrieve fragments . Existing systems store isolated data and fragments, limiting their ability to consolidate evolving experience and resolve conflicts.
Approach:	They propose an engram-inspired memory operating system that implements an 'engram'-inspired lifecycle for computational memory.
Outcome:	Experiments on LoCoMo, LongMemEval, and PersonaMeM-v2 show that EverMemeOS outperforms state-of-the-art methods on memory-augmented reasoning tasks.

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Kaiqi Yang, Hang Li, Hongzhi Wen, Tai-Quan Peng, Jiliang Tang, Hui Liu

Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)

Challenge:	Existing studies suggest that Large Language Models can generate human-like responses, but it is unclear how well they work and where the plausible predictions derive from.
Approach:	They propose to use LLMs to generate human-like responses by mutability and accessibility of social inputs to perform a social prediction task.
Outcome:	The proposed model performs well in three realistic settings and a novel social prediction task.

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Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu

Breaking Language Preference in Multilingual RAG via Language-Controllable Retrieval and Language-Agnostic Reasoning (2026.findings-acl)

Challenge:	Large language models struggle with complex reasoning tasks, such as mathematical problem-solving.
Approach:	They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning.
Outcome:	The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters.

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Wenshuai Huo, Xiaocheng Feng, Baohang Li, Chengpeng Fu, Yichong Huang, Hui Wang, Bing Qin

Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning (2024.emnlp-main)

Challenge:	Existing approaches to improve retrieval accuracy and generation quality of large language models suffer from language preference.
Approach:	They propose a framework that explicitly disentangles multilingual RAG into language-controllable retrieval and language-agnostic reasoning.
Outcome:	Experimental results show that the proposed approach outperforms baselines across multilingual benchmarks.

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Qizhou Chen, Taolin Zhang, Xiaofeng He, Dongyang Li, Chengyu Wang, Longtao Huang, Hui Xue’

A General Framework to Enhance Fine-tuning-based LLM Unlearning (2025.findings-acl)

Challenge:	Existing methods to correct outdated or erroneous knowledge in large language models (LLMs) are slow and cumbersome, resulting in catastrophic knowledge forgetting and degradation of model performance.
Approach:	They propose a RetriEval-augmented ContInuous Prompt lEarning method that converts knowledge statements into short and informative continuous prompts, prefixed to the LLM’s input query embedding.
Outcome:	The proposed method improves the performance of large language models (LLMs) while maintaining the overall performance of the model.

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Jie Ren, Zhenwei Dai, Xianfeng Tang, Hui Liu, Jingying Zeng, Zhen Li, Rahul Goutam, Suhang Wang, Yue Xing, Qi He, Hui Liu

MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)

Challenge:	Existing approaches to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs) have been proposed to remove specific data from LLMs without requiring full retraining.
Approach:	They propose a general framework that enhances the utility of fine-tuning-based methods by distinguishing target data and suppressing related generations.
Outcome:	The proposed framework improves the unlearning and utility of fine-tuning-based methods by distinguishing the target data and suppressing related generations.

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Hui Huang, Jiaheng Liu, Yancheng He, Shilong Li, Bing Xu, Conghui Zhu, Muyun Yang, Tiejun Zhao

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)

Challenge:	Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application.
Approach:	They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model.
Outcome:	The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity.

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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li

PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts (2023.acl-long)

Challenge:	Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping .
Approach:	They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations.
Outcome:	The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets.

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Yunshui Li, Binyuan Hui, ZhiChao Yin, Min Yang, Fei Huang, Yongbin Li

Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios (2026.acl-long)

Challenge:	Existing research on multi-modal dialogue pre-training is limited due to limited availability of multi-dimensional data . a recent emergence of chatGPT 1 has increased confidence in the potential for this goal .
Approach:	They propose a framework for multi-modal dialogue pre-training that integrates experts to accommodate multi-faceted tasks.
Outcome:	The proposed framework achieves state-of-the-art on eight multi-modal dialog benchmarks.

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Hui Liu, Bin Zou, Kecheng Chen, Jie Liu, Wenya Wang, Haoliang Li

Challenge:	Existing routers generalize poorly in cold-start scenarios where in-domain training data is unavailable.
Approach:	They propose a task-type–aware router approach that models query-conditioned cost and performance via latent task-like variables with prior regularization derived from the synthesized task taxonomy.
Outcome:	The proposed framework improves performance and cost under cold-start and in-domain settings and enables efficient routing.

MultiConIR: Towards Multi-Condition Information Retrieval (2025.findings-emnlp)

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Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li, Xinghao Chen, Hui Su, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen

Information Retrieval

Challenge:	MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.
Approach:	They propose a benchmark to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.
Outcome:	The proposed benchmark evaluates retrieval and reranking models under nuanced multi-condition query scenarios across five domains.

How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training (2025.findings-acl)

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Yixin Ou, Yunzhi Yao, Ningyu Zhang, Hui Jin, Jiacheng Sun, Shumin Deng, Zhenguo Li, Huajun Chen

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

Challenge:	Large Language Models (LLMs) have exceptional capabilities in knowledge-intensive tasks . however, they struggle with knowledge updates due to dynamic nature of world knowledge .
Approach:	They propose to identify computational subgraphs that facilitate knowledge storage and processing . they also identify a phase shift from formation to optimization in LLMs .
Outcome:	The proposed model can capture factual knowledge from pre-training corpus and encapsulate it as extensive parametric knowledge.

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

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

On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models (2024.acl-short)

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Dongyang Li, Junbing Yan, Taolin Zhang, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang

SVD-GCL: A Noise-Augmented Hybrid Graph Contrastive Learning Framework for Recommendation (2025.coling-main)

Challenge:	Existing RAG methods focus on improving the task performance, without fine-grained process of knowledge.
Approach:	They propose a method that detects long-tail knowledge in large language models by analyzing retrieved documents and enhancing queries indiscriminately with retrieved information.
Outcome:	The proposed method achieves over 4x speedup in average inference time and consistent performance improvement in downstream tasks compared to existing pipelines.

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Liping Wang, Shichao Li, Hui Wang, Yuyan Gao, Mingyao Wei

One-Shot Learning as Instruction Data Prospector for Large Language Models (2024.acl-long)

Challenge:	Recent advances in graph neural networks have made it difficult to capture user preferences.
Approach:	They propose a graph contrastive learning recommendation model based on noise augmentation that integrates truncated singular value decomposition in the feature engineering stage.
Outcome:	The proposed model reduces dimensionality and denoises the original data.

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Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

TrojanSQL: SQL Injection against Natural Language Interface to Database (2023.emnlp-main)

Challenge:	Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality.
Approach:	They propose a method that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets.
Outcome:	Nuggets outperforms existing methods on MT-Bench and Alpaca-Eval benchmarks.

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Jinchuan Zhang, Yan Zhou, Binyuan Hui, Yaxin Liu, Ziming Li, Songlin Hu

TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection (2024.findings-acl)

Challenge:	Existing studies on text-to-SQL systems have not investigated its security aspects . however, how to implement such attacks remains an open question.
Approach:	They propose a backdoor-based SQL injection framework for text-to-SQL systems that uses boolean-based injection and union-based injecting techniques to exploit SQL injection vulnerabilities.
Outcome:	The proposed framework can produce harmful SQL statements invalidating user queries or compromise sensitive information about the database.

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Hui Liu, Wenya Wang, Haoru Li, Haoliang Li

Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)

Challenge:	Existing methods for detecting fake news are limited due to non-transparent reasoning processes and inherent risks of integration with large language models.
Approach:	They propose a framework for trustworthy fake news detection that prioritizes explainability, generalizability and controllability of models.
Outcome:	The proposed framework prioritizes explainability, generalizability and controllability of models.

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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang

Extending LLM Context Window with Adaptive Grouped Positional Encoding: A Training-Free Method (2025.acl-long)

Challenge:	Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments.
Approach:	They propose a multimodal approach that integrates audio, visual, and semantic cues to enhance speaker diarization.
Outcome:	The proposed approach outperforms state-of-the-art methods on multi-party conversations . it integrates audio-visual-semantic cues into the clustering process for acoustic speaker embeddings .

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Xinhao Xu, Jiaxin Li, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)

Challenge:	Existing long-context training data is scarce and requires substantial GPU resources for training.
Approach:	They propose a training-free plug-and-play method to enhance long-context understanding in existing large language models.
Outcome:	The proposed method outperforms existing LLMs on various tasks and surpasses baseline methods.

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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He

AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data.
Approach:	They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation.
Outcome:	Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%.

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Wei Liu, Yancheng He, Yu Li, Hui Huang, Chengwei Hu, Jiaheng Liu, Shilong Li, Wenbo Su, Bo Zheng

Complementary Evidence Identification in Open-Domain Question Answering (2021.eacl-main)

Challenge:	Existing methods for generating complex instructions are resource-intensive and lack diversity.
Approach:	They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance.
Outcome:	The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods.

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Xiangyang Mou, Mo Yu, Shiyu Chang, Yufei Feng, Li Zhang, Hui Su

Advancing SMoE for Continuous Domain Adaptation of MLLMs: Adaptive Router and Domain-Specific Loss (2025.acl-long)

Challenge:	Existing approaches to QA that only measure the relevance between the question and each paragraph are not effective.
Approach:	They propose a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages.
Outcome:	The proposed method significantly improves the accuracy of complementary evidence selection in open-domain question answering domain.

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Liang Zhang, Ziyao Lu, Fandong Meng, Hui Li, Jie Zhou, Jinsong Su

GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery (2026.acl-long)

Challenge:	Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLM are required to continuously acquire new tasks.
Approach:	They propose a Sparse Mixture of Expert (SMoE) based method for domain-incremental CIT in Multimodal Large Language Models (MLLMs) . they equip the SMoA module with a domain-specific autoregressive loss (DSAL) they establish a new benchmark to evaluate the efficacy of their method .
Outcome:	The proposed method outperforms all baselines and is based on a Sparse Mixture of Experts (SMoE) module .

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Xi Chen, Chuan Qin, Jinpeng Li, Shasha Hu, Chao Wang, Hengshu Zhu, Hui Xiong

Challenge:	Existing methods rely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones.
Approach:	They propose a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM.
Outcome:	The proposed framework outperforms existing methods on five benchmarks on the generalized category discovery (GCD) task.

SEC-FinTables: Evaluating Large Language Models for Detecting Logical Inconsistencies on Tabular Data (2026.findings-acl)

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Shuyan Ke, Qiong Wu, Hui Li, Liujuan Cao

Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events (2026.acl-long)

Challenge:	Large language models are increasingly deployed in high-stakes domains where logical inconsistencies are unrecognized.
Approach:	They propose a benchmarking system that decomposes inconsistency detection into granular subtasks and a protocol that decompiles it into subtask.
Outcome:	The proposed model decomposes inconsistencies into subtasks and identifies them in 103,395 real-world and error-injected table instances.

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Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin

Challenge:	A critical bottleneck is the lack of ground-truth human data to link personality traits to emotional shifts.
Approach:	They propose a large-scale dataset to capture reader-based emotional variations across news, social media, and life narratives.
Outcome:	The proposed model captures reader-based emotional variations across news, social media, and life narratives.

CSL: A Large-scale Chinese Scientific Literature Dataset (2022.coling-1)

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Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, Hui Zhang

Information Extraction & Text Mining

Challenge:	Existing datasets centered around the English language restrict development of Chinese scientific NLP.
Approach:	They present a large-scale Chinese scientific literature dataset based on Chinese papers . they use semi-structured data as a natural annotation for many supervised NLP tasks .
Outcome:	The proposed dataset can serve as a Chinese corpus and perform many supervised tasks.

Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models (2024.findings-emnlp)

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Sheng Zhang, Hui Li, Rongrong Ji

Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement (2022.emnlp-main)

Challenge:	Code pre-trained language models (CPLMs) are trained on open-source code, raising concerns about data infringement.
Approach:	They propose a framework for detecting unauthorized code use in CPLMs . they use signal extraction from pre-training tasks and weighted inference to identify code membership status accurately.
Outcome:	The proposed framework detects unauthorized code use with high accuracy.

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Hui Liu, Wenya Wang, Haoliang Li

Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)

Challenge:	Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions.
Approach:	They propose a hierarchical framework for sarcasm detection by exploring atomic-level congruity and composition-level convergence.
Outcome:	The proposed model outperforms existing methods on a public sarcasm detection dataset based on Twitter .

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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen

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

Challenge:	Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach:	They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
Outcome:	The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing.

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

History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling (2023.acl-long)

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

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Hao Sun, Yang Li, Liwei Deng, Bowen Li, Binyuan Hui, Binhua Li, Yunshi Lan, Yan Zhang, Yongbin Li

SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context (2026.findings-acl)

Challenge:	Existing methods for conversational KBQA assume the independence of utterances and model them in isolation.
Approach:	They propose a History Semantic Graph Enhanced KBQA model that models long-range semantic dependencies in conversation history while maintaining low computational cost.
Outcome:	The proposed model outperforms baselines on a widely used question type dataset.

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Shuquan Lian, Juncheng Liu, Yazhe Chen, Yuhong Chen, Hui Li

PEAR: Planner-Executor Agent Robustness Benchmark (2026.findings-eacl)

Challenge:	representative ReAct-style approaches lack explicit System-2 reasoning for deep analysis and handling complex edge cases.
Approach:	They propose a software agent framework that preserves full reasoning history while compressing historical reasoning content into concise Reasoning Digests.
Outcome:	Empirically, the proposed framework sets a new standard for 7B-8B models on SWE-Bench-Verified using only 2.2k trajectories and 896 tasks.

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Shen Dong, Mingxuan Zhang, Pengfei He, Li Ma, Bhavani Thuraisingham, Hui Liu, Yue Xing

Robustness in NLP

Challenge:	Existing studies examine isolated attack surfaces or specific scenarios, leaving a lack of holistic understanding of MAS vulnerabilities.
Approach:	They propose a benchmark to evaluate the utility and vulnerability of planner–executor MAS.
Outcome:	The proposed benchmark evaluates planner–executor MAS on a widely adopted design.

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

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

Domain-Specific Data Generation Framework for RAG Adaptation (2026.findings-acl)

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

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Chris Xing Tian, Weihao Xie, Zhen Chen, Hui Liu, Zhengyuan Yi, Haoliang Li, Shiqi Wang, Siwei Ma

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses.
Approach:	They propose a scalable and modular data-centric framework for generating domain-grounded question–answer–context triples tailored to diverse RAG adaptation strategies.
Outcome:	The proposed framework generates domain-grounded question–answer–context triples for multiple RAG adaptation strategies.

Refiner: Restructure Retrieved Content Efficiently to Advance Question-Answering Capabilities (2024.findings-emnlp)

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Zhonghao Li, Xuming Hu, Aiwei Liu, Kening Zheng, Sirui Huang, Hui Xiong

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks.
Approach:	They propose an end-to-end extract-and-restructure paradigm that leverages a single decoder-only LLM to adaptively extract query-relevant contents verbatim along with the necessary context.
Outcome:	Experiments show that a trained Refiner outperforms state-of-the-art RAG and compressing approaches in multiple tasks.

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

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

HSCodeComp: A Realistic and Expert-level Agent Benchmark for Hierarchical Rule Application (2026.acl-long)

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

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Tian Lan, Yiqian Yang, Qianghuai Jia, Li Zhu, Hui Jiang, Hang Zhu, Weihua Luo, Longyue Wang

RoChBert: Towards Robust BERT Fine-tuning for Chinese (2022.findings-emnlp)

Challenge:	Existing agent benchmarks neglect hierarchical rule application in real-world domains . a critical gap persists in numerous real-life professional domains where decision-making is governed by expert-written rules.
Approach:	They propose a benchmark requiring agents to assign a unique 10-digit Harmonized System (HS) Code to products by aligning their fuzzy attributes with strict tariff classification rules.
Outcome:	The proposed benchmarks lack hierarchical rule application capability in real-world domains . the proposed benchmark is based on e-commerce and is open-source .

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Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang

Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning (2025.acl-long)

Challenge:	Pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts.
Approach:	They propose to fuse Chinese phonetic and glyph features into pre-trained models by using a more comprehensive adversarial graph.
Outcome:	The proposed framework outperforms existing methods in significant ways on a wide range of tasks while remaining accurate on benign texts.

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Hui Liu, Wenya Wang, Hao Sun, Chris Xing Tian, Chenqi Kong, Xin Dong, Haoliang Li

Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)

Challenge:	Recent learning-based demonstration selection methods have proven beneficial to in-context learning (ICL) by choosing more useful exemplars.
Approach:	They propose two methods to capture task-agnostic similarities between input and output of LLMs.
Outcome:	The proposed methods integrate task-agnostic similarities of different levels between input and output of exemplars and test cases to eliminate costly data collection.

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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu

Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)

Challenge:	Large language models extract useful information from conversation history to enhance the response in long-term conversations.
Approach:	They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation.
Outcome:	The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation .

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Bei Li, Ziyang Wang, Hui Liu, Yufan Jiang, Quan Du, Tong Xiao, Huizhen Wang, Jingbo Zhu

Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)

Challenge:	Experimental results show that deep training is 1:4 faster than training from scratch.
Approach:	They propose a shallow-to-deep training method that learns deep models by stacking shallow models.
Outcome:	The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks.

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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He

Learning Transition Patterns by Large Language Models for Sequential Recommendation (2025.coling-main)

Challenge:	Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states.
Approach:	They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM.
Outcome:	The proposed framework outperforms strong baselines in performance and efficiency.

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Jianyang Zhai, Zi-Feng Mai, Dongyi Zheng, Chang-Dong Wang, Xiawu Zheng, Hui Li, Feidiao Yang, Yonghong Tian

Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (2025.findings-emnlp)

Challenge:	Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10 and 8.42% in MRR.
Approach:	They propose a framework for mapping sequential item texts to sequential item IDs that incorporates multi-query input and item linear projection to model conditional probability distribution of items.
Outcome:	The proposed framework outperforms baseline models on six real-world datasets by 7.33% and 4.65% respectively.

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Yuanchi Ma, Jiamou Liu, Hui He, Libo Zhang, Haoyuan Li, Zhendong Niu

Challenge:	Existing methods for generating readable outlines are inability to segment long texts .
Approach:	They propose an unsupervised framework to guide large language model outline generation . framework ensures each structured plot encapsulates complete causality by accurately identifying plot boundaries.
Outcome:	The proposed framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries.

Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)

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Suhuang Wu, Huimin Wang, Yutian Zhao, Xian Wu, Yefeng Zheng, Wei Li, Hui Li, Rongrong Ji

SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding (2022.coling-1)

Challenge:	Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails.
Approach:	They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks.
Outcome:	The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses.

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Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li

Pru-CoT: Towards Efficient Reasoning Distillation via Pruning Chain-of-Thought (2026.findings-acl)

Challenge:	Existing methods for dialog understanding only consider self-augmented dialogs as positive samples and treat all other dialogs like negative ones.
Approach:	They propose a tree-structured pre-trained conversation model which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training.
Outcome:	The proposed model can achieve state-of-the-art results on the DialoGLUE benchmark.

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Han Liu, Shuotian Ma, Hui Li, Xiaotong Zhang, Fenglong Ma, Hong Yu

WinSpot: GUI Grounding Benchmark with Multimodal Large Language Models (2025.acl-short)

Challenge:	Existing heuristics fail to capture global causal logic due to rigid rules and limited search spaces.
Approach:	They propose a framework that extracts the essential logical structure from reasoning chains.
Outcome:	Experiments show that Pru-CoT models generate more compact reasoning paths compared to models trained on verbose data.

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Zheng Hui, Yinheng Li, Dan Zhao, Colby Banbury, Tianyi Chen, Kazuhito Koishida

S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers (2022.findings-acl)

Challenge:	Existing GUI grounding data focuses on web-based elements, leaving a gap in real-world GUI interaction data for non-web applications.
Approach:	They propose a framework that leverages Large Language Models to generate large-scale GUI grounding data.
Outcome:	The framework validates and refines 5,000 GUI coordinate-instruction pairs and provides high-quality data for training and evaluating visual GUI agents.

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Binyuan Hui, Ruiying Geng, Lihan Wang, Bowen Qin, Yanyang Li, Bowen Li, Jian Sun, Yongbin Li

On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation (2022.naacl-main)

Challenge:	Existing graph-based encoders for text-to-SQL do not model the syntax of natural language questions.
Approach:	They propose to inject Syntax to question-Schema graph encoder for text-to-SQL parsers and employ the decoupling constraint to induce diverse relational edge embedding.
Outcome:	The proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard.

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Yongjie Wang, Chuang Wang, Ruobing Li, Hui Lin

Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2025.acl-long)

Challenge:	Pre-trained models have not been used to outperform other deep learning models such as CNN in Automated Essay Scoring (AES).
Approach:	They propose a novel multi-scale essay representation for BERT that can be jointly learned . they employ multiple losses and transfer learning from out-of-domain essays to further improve performance .
Outcome:	The proposed model outperforms existing models in the area of automated essay scoring . the proposed model generalizes well to the CommonLit Readability Prize data set .

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Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, Weixun Wang, Hui Huang, Xingyuan Bu, Hangyu Guo, Chengwei Hu, Boren Zheng, Zhuoran Lin, Dekai Sun, Zhicheng Zheng, Wenbo Su, Bo Zheng

EMA: An Episodic Memory Agent for Efficient and Selective Memory (2026.findings-acl)

Challenge:	Current frontier models sometimes generate false outputs or answers that are not substantiated by evidence.
Approach:	They propose Chinese SimpleQA, a Chinese benchmark to evaluate LLMs' factuality . they focus on Chinese language over 6 major topics with 99 diverse subtopics .
Outcome:	The Chinese SimpleQA benchmark evaluates the factuality ability of LLMs . the questions and answers are short and easy-to-evaluate .

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Hongyi Lan, Jiaqi Song, Zhengjia Zhong, Hui Li, Hong Liu, Xianming Lin, Rongrong Ji

HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy (2026.findings-acl)

Challenge:	Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency.
Approach:	They propose a framework that abstracts conversational context into Episodic Memory Units (EMUs) they propose EMA, MemDecider and a filtering decision module to reduce noise and improve overall performance.
Outcome:	The proposed framework reduces token consumption by 11.48% while improving performance on two widely-used benchmarks.

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Guoqi Ma, Liang Zhang, Hongyao Tu, Hao Fu, Hui Li, Yujie Lin, Longyue Wang, Weihua Luo, Jinsong Su

Information Extraction & Text Mining

Challenge:	Existing approaches to cross-document relation extraction (RE) focus on identifying relations between head and tail entities from single sentence or document.
Approach:	They propose a hierarchical relation tree-based LLM-based hierarchic classification model for cross-document relation extraction (HCRE) based on predefined relations, the model can perform hierarchically classification level by level.
Outcome:	The proposed model outperforms existing baselines and validates its effectiveness.

MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization (2024.lrec-main)

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Tao Chen, Ze Lin, Hui Li, Jiayi Ji, Yiyi Zhou, Guanbin Li, Rongrong Ji

SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs (2026.acl-long)

Challenge:	Existing product summarization methods lack end-to-end product summaries and multi-grained multi-modal modeling.
Approach:	They propose an end-to-end multi-grained multi-modal attribute-aware product summarization method that jointly models product attributes and generates product summaries.
Outcome:	The proposed method outperforms state-of-the-art product summarization methods on a large-scale Chinese e-commence dataset.

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Yanxiao Zhao, Yaqian Li, Zi-Hao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, null Renlei, Xiaolin Qin, Kaiwen Long

From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)

Challenge:	Large language models exhibit strong general reasoning abilities, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve them.
Approach:	They propose a verifier that generates diverse SAT-based reasoning tasks from CNF instances and checks answers objectively with PySAT.
Outcome:	The proposed verifier generates diverse SAT-based reasoning tasks from CNF instances and checks answers objectively with PySAT.

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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

Challenge:	Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination.
Approach:	They propose a MOS-aware reward model that incorporates MOS gap into reward function during reinforcement learning.
Outcome:	The proposed model significantly improves fine-grained speech quality discrimination.

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning (2025.findings-acl)

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Haoyu Han, Yaochen Xie, Hui Liu, Xianfeng Tang, Sreyashi Nag, William Headden, Yang Li, Chen Luo, Shuiwang Ji, Qi He, Jiliang Tang

MoEC: A Memory-Routed Mixture-of-Experts Controller for Adaptive Minecraft Control (2026.acl-long)

Challenge:	Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks, however, they still face challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences.
Approach:	They propose to construct explicit graphs from context and leverage them to enhance LLM reasoning performance on reasoning tasks.
Outcome:	Extensive experiments show that the proposed method improves both logical reasoning and multi-hop question answering tasks.

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Hui Wu, Chao Xu, Jianghui Wang, Ziqiong Liu, Dong Li, Yiwei Dai, Emad Barsoum

ContrastKV: Robust KV Cache Eviction via Contrastive Signal Fusion for Multi-Query Generalization (2026.acl-long)

Challenge:	Existing systems rely on a monolithic policy to execute subgoals across varying contexts, causing inconsistent outcomes and scaling only partially mitigates.
Approach:	They propose a memory-routed mixtureof-experts controller for Adaptive Minecraft Control that routes via a subgoal-indexed expert memory and regulates capacity through failure-triggered expert growth and redundancy-aware consolidation.
Outcome:	The proposed controller shows significant gains in adaptability, robustness, and execution consistency over strong baselines.

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Xingchi Chen, Peiyuan Zong, Ziqiang Gao, Qing Li, Yong Jiang, Fa Zhu, Hui Li

Robustness in NLP

Challenge:	Existing query-agnostic approaches rely on a single proxy query, leading to fragile eviction decisions under high evict ratios.
Approach:	They propose a query-agnostic KV cache eviction algorithm that exploits complementary semantic and non-semantic signals.
Outcome:	Experiments show that the proposed algorithm outperforms state-of-the-art methods while retaining up to 92% accuracy with only 20% of the KV cache budget.

AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)

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Kaibo Huang, Jin Tan, Yukun Wei, Wanling Li, Zipei Zhang, Hui Tian, Zhongliang Yang, Linna Zhou

Challenge:	Recent advances in large language models (LLMs) have improved text generation and reasoning.
Approach:	They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility.
Outcome:	The proposed framework embeds multi-bit provenance into planning decisions while preserving utility.

Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)

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Bingyu Wang, Li Chen, Wei Sun, Kechen Qin, Kefeng Li, Hui Zhou

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)

Challenge:	Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods .
Approach:	They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems.
Outcome:	The proposed method is competitive to state-of-the-art methods on benchmark datasets.

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Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark (2023.findings-acl)

Challenge:	Existing question reformulation models are based on supervised question labels without considering feedback information from answers.
Approach:	They propose a question reformulation model that integrates conversational history information with reinforcement learning.
Outcome:	The proposed model is more effective in conversational machine comprehension with reinforcement learning.

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Yuxing Long, Binyuan Hui, Caixia Yuan, Fei Huang, Yongbin Li, Xiaojie Wang

Interpretable Multimodal Misinformation Detection with Logic Reasoning (2023.findings-acl)

Challenge:	Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario.
Approach:	They propose a multimodal task-oriented dialog dataset with subjective preferences and recommendation acts that is well-annotated with sales experts.
Outcome:	The proposed model is powered by a state-of-the-art multimodal model for these tasks.

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Hui Liu, Wenya Wang, Haoliang Li

Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment (2024.lrec-main)

Challenge:	Existing methods for misinformation detection lack interpretability due to the black-box nature of the neural network.
Approach:	They propose a logic-based neural model which integrates interpretable logic clauses to express the reasoning process of the target task.
Outcome:	The proposed model can be generalizable across multiple misinformation sources and is based on three public datasets.

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Yingxiu Zhao, Bowen Yu, Binyuan Hui, Haiyang Yu, Minghao Li, Fei Huang, Nevin L. Zhang, Yongbin Li

Challenge:	Extensive research has highlighted the importance of data complexity as a crucial metric, but the impact of complexity remains relatively unexplored.
Approach:	They propose to add a specified number of nodes to instructions’ semantic trees to enhance the instruction complexity in a controllable manner.
Outcome:	The proposed approach outperforms diverse yet complex instructions under the same token budget and can control the difficulty level of modified instructions.

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)

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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang

Better Late Than Never: Model-Agnostic Hallucination Post-Processing Framework Towards Clinical Text Summarization (2024.findings-acl)

Challenge:	Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows .
Approach:	They propose a repository-level evaluation benchmark to assess security of AI-generated code.
Outcome:	The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation.

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Songda Li, Yunqi Zhang, Chunyuan Deng, Yake Niu, Hui Zhao

UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding (2024.lrec-main)

Challenge:	Existing methods for generating concise and coherent summaries may include unintended text with hallucinations, causing computational costs.
Approach:	They propose a model-agnostic framework to post-process medical hallucinations . MEDAL integrates with any medical summarization model, requiring no additional computational overhead .
Outcome:	MEDAL can post-process medical hallucinations without additional computational overhead.

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Dongyang Li, Taolin Zhang, Jiali Deng, Longtao Huang, Chengyu Wang, Xiaofeng He, Hui Xue

Low-Resource NLP

Challenge:	Existing studies rely on shallow unsupervised data generated by token surface matching regardless of global context-aware semantics of the surrounding text tokens.
Approach:	They propose an Unsupervised Pseudo Semantic Data Augmentation mechanism to enrich training data without human intervention.
Outcome:	The proposed model improves on general zero-shot cross-lingual understanding tasks on different languages without human intervention.

SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)

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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin