Papers by He Feng

Composable Text Controls in Latent Space with ODEs (2023.emnlp-main)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

Copied to clipboard

Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu

Green & Sustainable NLP

Challenge:	Existing approaches to composable text operations often require plug-and-play . a single LM can perform arbitrary text operation composition in the latent space .
Approach:	They propose an efficient approach for composable text operations in the latent space of text . they connect pretrained LMs to the laten space and adapt them to the space .
Outcome:	The proposed approach improves on existing methods in the latent space of text.

Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)

Copied to clipboard

Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang

Beyond Online Sampling: Bridging Offline-to-Online Alignment via Dynamic Data Transformation for LLMs (2025.emnlp-main)

Challenge:	Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation.
Approach:	They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD).
Outcome:	The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline.

Copied to clipboard

Zhang Zhang, Guhao Feng, Jian Guan, Di He, Wei Wu

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)

Challenge:	Direct Preference Optimization (DPO) eliminates complex reward modeling in aligning large language models with human preferences, but its online variant faces significant efficiency bottlenecks due to costly real-time preference sampling and the reward model annotation.
Approach:	They propose a framework that transforms static datasets into dynamically adaptive equivalents without the need for an explicit reward model.
Outcome:	The proposed approach matches or exceeds the performance of a fully online DPO.

Copied to clipboard

Yuhan Kang, Yang Shi, Mei Wen, Jun He, Jianchao Yang, Zeyu Xue, Jing Feng, Xinwang Liu

Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)

Challenge:	a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations.
Approach:	They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting .
Outcome:	The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations.

Copied to clipboard

Qika Lin, Tianzhe Zhao, Kai He, Zhen Peng, Fangzhi Xu, Ling Huang, Jingying Ma, Mengling Feng

ChatAnime: Towards User-Centered Emotional Support in LLM-based Virtual Character Chat (2026.acl-long)

Challenge:	Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model.
Approach:	They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences.
Outcome:	The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks.

Copied to clipboard

Lanlan Qiu, Sophia Xiao Pu, Yeqi Feng, Wenchang Gao, Tianxing He

Natural Language Interfaces

Challenge:	Existing research focuses on character consistency in fictional or game-based scenarios . ESRP framework is designed to align role-playing with real-world user scenarios based on emotional needs.
Approach:	They propose a framework to align role-playing with real-world user scenarios and emotional needs.
Outcome:	The proposed framework aligns role-playing with real-world user scenarios and emotional needs.

Personalized Generation In Large Model Era: A Survey (2025.acl-long)

Copied to clipboard

Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, Tat-Seng Chua

Text Generation

Challenge:	Recent advances in large generative models have catalyzed a paradigm shift in content generation to Personalized Generation (PGen).
Approach:	They propose a multi-level taxonomy that systematically formalizes PGen's key components, core objectives, and abstract workflows.
Outcome:	The proposed taxonomy bridging PGen research across multiple modalities highlights open challenges and promising directions for future exploration.

LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation (2022.tacl-1)

Copied to clipboard

Jian Guan, Zhuoer Feng, Yamei Chen, Ruilin He, Xiaoxi Mao, Changjie Fan, Minlie Huang

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (2020.acl-main)

Challenge:	Existing benchmarks for natural language processing focus on understanding or generating short texts . lack of standardized benchmarks makes it difficult to assess and compare models .
Approach:	They propose a story-centric benchmark for Chinese long text modeling that aggregates two understanding tasks and two generation tasks.
Outcome:	The proposed model outperforms similar-sized models on understanding and generation tasks.

Copied to clipboard

Hao Tian, Can Gao, Xinyan Xiao, Hao Liu, Bolei He, Hua Wu, Haifeng Wang, Feng Wu

Automated Chinese Essay Scoring from Multiple Traits (2022.coling-1)

Challenge:	sentiment knowledge is ignored in sentiment analysis, despite its use in pretraining.
Approach:	They propose to use sentiment knowledge to learn a unified sentiment representation for multiple sentiment analysis tasks.
Outcome:	The proposed method outperforms strong pre-training baseline on three kinds of sentiment tasks.

Copied to clipboard

Yaqiong He, Feng Jiang, Xiaomin Chu, Peifeng Li

Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis (2024.findings-emnlp)

Challenge:	Current research on AES focuses on scoring the overall quality or single trait of prompt-specific essays.
Approach:	They propose a hierarchical multi-task trait scorer to evaluate quality of writing . they propose an inter-sequence attention mechanism to enhance information interaction .
Outcome:	The proposed model outperforms several strong models on ACEA and outperformed other models.

Copied to clipboard

Xinyu Feng, Yuming Lin, Lihua He, You Li, Liang Chang, Ya Zhou

Attack Prompt Generation for Red Teaming and Defending Large Language Models (2023.findings-emnlp)

Challenge:	Existing methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modal may become dominant.
Approach:	They propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) that uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality.
Outcome:	The proposed model can be used to highlight the contribution of dominant modality through the correlation evaluation loss.

Copied to clipboard

Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He

Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)

Challenge:	Existing studies construct attack prompts via manual or automatic methods, but these methods have limitations on cost and quality.
Approach:	They propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning and a defense framework that fine-tunes victim LLM's through iterative interactions with the attack framework.
Outcome:	The proposed approach is based on experiments on different LLMs to evaluate their effectiveness against red teaming attacks.

Copied to clipboard

Yutao Mou, Keqing He, Yanan Wu, Pei Wang, Jingang Wang, Wei Wu, Yi Huang, Junlan Feng, Weiran Xu

Dialogue Systems & Conversational Agents

Challenge:	Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios.
Approach:	They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents.
Outcome:	The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally.

Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search (2026.acl-long)

Copied to clipboard

Wentao Shi, Zichun Yu, Fuli Feng, Xiangnan He, Chenyan Xiong

CPL: Counterfactual Prompt Learning for Vision and Language Models (2022.emnlp-main)

Challenge:	Large Language Model (LLM) based multi-agent systems (MAS) have high potential for tackling complex tasks through collaborative intelligence.
Approach:	They propose a framework that incorporates influence scores to guide tree search and data selection in data synthesis.
Outcome:	The proposed framework incorporates influence scores to guide tree search and data selection in data synthesis.

Copied to clipboard

Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Wang

Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention (D19-1)

Challenge:	Existing prompt tuning methods tend to learn spurious or entangled representations, leading to poor generalization to unseen concepts.
Approach:	They propose a prompt tuning technique that tunes the learnable prompt for pre-trained vision and language models.
Outcome:	The proposed method improves few-shot performance on vision and language tasks over existing prompt tuning methods.

Copied to clipboard

Lei Cao, Huijun Zhang, Ling Feng, Zihan Wei, Xin Wang, Ningyun Li, Xiaohao He

MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)

Challenge:	Existing approaches to detect suicidal ideation on social media are limited to a small group of people.
Approach:	They propose to use tree holes to embed words into microblogs to strengthen the sensibility of suicide-related lexicons and to use a two-layered attention mechanism to grasp intermittently changing points from individual's open blog streams.
Outcome:	The proposed approach can achieve over 91% accuracy with the use of suicide-oriented word embeddings and attention on a large-scale well-labelled suicide data set.

Copied to clipboard

Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao

Natural Language Interfaces

Challenge:	Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions.
Approach:	They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models.
Outcome:	The proposed benchmarks show that the models perform better in open-ended conversations.

Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)

Copied to clipboard

Yichen Wang, Shangbin Feng, Abe Hou, Xiao Pu, Chao Shen, Xiaoming Liu, Yulia Tsvetkov, Tianxing He

Robustness in NLP

Challenge:	Existing studies on this topic focus on the robustness of specific detectors or particular attack methods.
Approach:	They stress test the detectors’ robustness to malicious attacks under realistic scenarios using LLMs and metric-based detectors.
Outcome:	The proposed methods are based on a set of LLM-based models and their performance is compared under different budget levels.

A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)

Copied to clipboard

He Zhao, Zhunchen Luo, Chong Feng, Anqing Zheng, Xiaopeng Liu

Text Classification

Challenge:	Existing academic search engines cannot detect relevant papers where a resource is mentioned.
Approach:	They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework .
Outcome:	The proposed model achieves the best results on both the classification task and recommendation task.

MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning (2026.acl-long)

Copied to clipboard

Zhihui Chen, Kai He, Qingyuan Lei, Bin Pu, Jian Zhang, Yuling Xu, Mengling Feng

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

Challenge:	Existing defenses against forgery are inadequate for healthcare.
Approach:	They propose a large-scale benchmark for pre-hoc, evidence-grounded medical forgery detection using a doctor inspection guideline and gold edit locations.
Outcome:	Experiments show that the proposed solution can detect and explain medical scans with high fidelity and accuracy.

Copied to clipboard

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 .

DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection (2022.findings-emnlp)

Copied to clipboard

Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, Zhifang Sui

Natural Language Interfaces

Challenge:	DialogUSR is a plug-in and domain-agnostic module that empowers multi-intent detection for chatbots . a single user query triggers inquiries on highspeed train ticket price and weather of destination.
Approach:	They propose a dialog utterance splitting and reformulation task that splits multi-intent user query into multiple single-intention sub-queries and recovers all coreferred and omitted information in the sub-questions.
Outcome:	The proposed model can be used to split multi-intent user queries into multiple sub-queries . it can be trained in two stages and perform in-depth analyses on the proposed models .

LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)

Copied to clipboard

Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator (2023.findings-acl)

Challenge:	Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics.
Approach:	They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks.
Outcome:	The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring.

Copied to clipboard

Ziwei He, Meng Yang, Minwei Feng, Jingcheng Yin, Xinbing Wang, Jingwen Leng, Zhouhan Lin

PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)

Challenge:	Existing transformer models are computationally demanding and prohibitively costly for long sequences due to the quadratic complexity of its selfattention module.
Approach:	They propose a transformer-based model that inherits weights from large pretrained models by removing redundancies in hidden sequences using the ready-made Fast Fourier Transform operator.
Outcome:	The proposed model outperforms the standard BART model on the long-range modeling benchmark LRA with significant improvements in speed and space.

Copied to clipboard

Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu

Robustness in NLP

Challenge:	Existing slot filling models memorize inherent patterns of entities and contexts from training data.
Approach:	They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution .
Outcome:	The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts.

Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)

Copied to clipboard

Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

Challenge:	Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process .
Approach:	They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation.
Outcome:	The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench.

Copied to clipboard

Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships (2021.findings-emnlp)

Copied to clipboard

Lei He, Suncong Zheng, Tao Yang, Feng Zhang

Rethinking Text-based Protein Understanding: Retrieval or LLM? (2025.emnlp-main)

Challenge:	Existing knowledge-enhanced pretrained language models focus on entity information and ignore fine-grained relationships between entities.
Approach:	They propose to incorporate KG into the language learning process to obtain a KG-enhanced pretrained Language Model.
Outcome:	The proposed model improves on several knowledge-driven tasks, such as entity typing and relation classification, compared with the state-of-the-art knowledge-enhanced PLMs.

Copied to clipboard

Juntong Wu, Zijing Liu, He Cao, Li Hao, Bin Feng, Zishan Shu, Ke Yu, Li Yuan, Yu Li

Information Retrieval

Challenge:	Recent studies have focused on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment.
Approach:	They propose a retrieval-enhanced method which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.
Outcome:	The proposed method significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.

RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model (2025.coling-main)

Copied to clipboard

Tengfei Feng, Liang He

Agentic Verification for Ambiguous Query Disambiguation (2026.findings-acl)

Challenge:	Existing methods for Knowledge Base Question Answering (KBQA) face hallucination problems, resulting in low accuracy.
Approach:	They propose a retrieval-generate-retrieve framework that uses a Retrieve-Generate framework to retrieve factual knowledge from a knowledge graph.
Outcome:	Experimental results show that RGR-KBQA improves on CWQ and WebQSP datasets.

Copied to clipboard

Youngwon Lee, Seung-won Hwang, Ruofan Wu, Feng Yan, Danmei Xu, Moutasem Akkad, Zhewei Yao, Yuxiong He

Information Retrieval

Challenge:	Prior Diversify-then-Verify pipelines generate interpretations and then retrieve evidence . ambiguous queries require RAG to disambiguate into interpretations that can be answered from corpus .
Approach:	They propose a novel approach that unifies diversification with verification by integrating retriever relevance and generator answerability feedback early.
Outcome:	The proposed approach improves grounding-aware F1 by 23% over baselines across multiple LLMs.

Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026.acl-long)

Copied to clipboard

Jiawei Zhou, Chi Zhang, Xiang Feng, Qiming Zhang, Haibo Qiu, Lihuo He, Dengpan Ye, Xinbo Gao, Jing Zhang

Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)

Challenge:	a benchmark is designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code.
Approach:	They propose a benchmark to evaluate the capability of Large Multimodal Models to convert digital graphics into executable code.
Outcome:	The proposed benchmark exposes the performance gap among leading LMMs . the benchmark features 1130 meticulously curated samples .

Copied to clipboard

Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin

Empowering Reliable Visual-Centric Instruction Following in MLLMs (2026.findings-acl)

Challenge:	Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment.
Approach:	They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning.
Outcome:	The proposed framework outperforms baselines and supports generalization across different model configurations and backbones.

Copied to clipboard

Weilei He, Feng Ju, Zhiyuan Fan, Rui Min, Minhao Cheng

DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

Challenge:	Existing benchmarks for evaluating instruction-following capabilities focus on verbal instructions in the textual modality.
Approach:	They propose to incorporate vision-dependent constraints into instruction design to enable a more rigorous assessment of how well MLLMs align their outputs with both visual input and textual instructions.
Outcome:	The proposed benchmark incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions.

Copied to clipboard

Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach:	They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome:	The proposed model outperforms baselines on three real-world datasets.

Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction (2021.naacl-main)

Copied to clipboard

Yuhao Feng, Yanghui Rao, Yuyao Tang, Ninghua Wang, He Liu

Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)

Challenge:	Recent studies on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction . Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly .
Approach:	They propose to use a pre-trained language model with multi-head self-attention to integrate TOWE with AOPE to extract aspects and opinion terms in pairs.
Outcome:	The proposed structure outperforms the benchmark methods on TOWE significantly . the proposed structure is similar or even better than state-of-the-art AOPE models .

Copied to clipboard

Jianfeng He, Runing Yang, Linlin Yu, Changbin Li, Ruoxi Jia, Feng Chen, Ming Jin, Chang-Tien Lu

Text Generation

Challenge:	Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods.
Approach:	They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets.
Outcome:	The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable.

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

Copied to clipboard

Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations (2023.acl-long)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

Copied to clipboard

Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	In-context learning is an important paradigm for adapting large language models to new tasks . but the generalization behavior of ICL remains poorly understood .
Approach:	They characterize the feature biases of large language models by constructing underspecified demonstrations . they find that LLMs exhibit clear feature bias, and they evaluate interventions .
Outcome:	The proposed model prefers the "default" task features over distractor features more often than the base model.

Copied to clipboard

Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

Copied to clipboard

Xuetao Tian, Liping Jing, Lu He, Feng Liu

Information Extraction & Text Mining

Challenge:	Existing methods for relational triple extraction still face challenges, including information loss and error propagation.
Approach:	They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them.
Outcome:	The proposed model outperforms the baselines on five public datasets.

AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation (2026.findings-acl)

Copied to clipboard

Wentao Shi, Yu Wang, Yuyang Zhao, Yuxin Chen, Fuli Feng, Xueyuan Hao, Xi Su, Qi GU, Hui Su, Xunliang Cai, Xiangnan He

Challenge:	Existing approaches to verify agent behaviors in complex environments rely on rule-based verifiers or LLM-as-a-Judge models.
Approach:	They propose a benchmark to evaluate Agent-as-a-Judge across three domains . the benchmark covers search, data systems, and graphical user interfaces - with 155 tasks and 516 trajectories .
Outcome:	The proposed benchmark outperforms existing benchmarks in search, data systems, and GUI domains while revealing open challenges in agent-based verification.

Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022.acl-long)

Copied to clipboard

Moxin Li, Fuli Feng, Hanwang Zhang, Xiangnan He, Fengbin Zhu, Tat-Seng Chua

Reasoning

Challenge:	Existing NDR models suffer from large performance drop on hypothetical questions, e.g., “what the annualized rate of return would be if the revenue in 2020 was doubled”.
Approach:	They propose a learning to imagine module which can be seamlessly incorporated into NDR models to perform the imagination of unseen counterfactual.
Outcome:	The proposed model can perform the imagination of unseen counterfactuals on hypothetical questions.

Can LLM Graph Reasoning Generalize beyond Pattern Memorization? (2024.findings-emnlp)

Copied to clipboard

Yizhuo Zhang, Heng Wang, Shangbin Feng, Zhaoxuan Tan, Xiaochuang Han, Tianxing He, Yulia Tsvetkov

Structured Data in NLP

Challenge:	Existing studies seek to enhance the graph reasoning capabilities of Large Language Models (LLMs) by specialized instruction tuning.
Approach:	They propose to evaluate LLM graph reasoning generalization using in-distribution settings . they propose to use three strategies to improve LLM generalization .
Outcome:	The proposed benchmark evaluates LLM graph reasoning generalization with in-distribution settings only . it shows that LLMs struggle to generalize across reasoning and real-world patterns .

Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph (2024.acl-long)

Copied to clipboard

Xiaochen Gao, Feng Yao, Kewen Zhao, Beilei He, Animesh Kumar, Vish Krishnan, Jingbo Shang

AlignX: Advancing Multilingual Large Language Models with Multilingual Representation Alignment (2025.emnlp-main)

Challenge:	Scaling up language models has demonstrated predictable improvement and unprecedented abilities in many language tasks.
Approach:	They propose a fine-grained cLAim depeNdency graph that captures the dependencies within the patent data and extends the embedding-based state-of-the-art (SOTA) they then explore prompt-based methods to harness proprietary LLMs' potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up.
Outcome:	The proposed graph methods outperform the standard model scaling methods in the patent approval prediction task and show that they are cost-effective.

Copied to clipboard

Mengyu Bu, Shaolei Zhang, Zhongjun He, Hua Wu, Yang Feng

Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models (2026.acl-long)

Challenge:	Multilingual large language models (LLMs) possess impressive multilingual understanding and generation capabilities, but performance and cross-lingual alignment often lag for non-dominant languages.
Approach:	They propose a representation-level framework to enhance multilingual performance of pre-trained LLMs by integrating multilingual semantic alignment and language feature integration.
Outcome:	The proposed framework improves multilingual capability of pre-trained LLMs by bringing representations closer and improving cross-lingual alignment.

Copied to clipboard

Yan Liu, Feng Zhang, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Han Liu, Yangdong Deng

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models (2026.findings-acl)

Challenge:	Existing approaches to mitigate inference inefficiency and optimization difficulty are fragmented and constrained by inherent trade-offs.
Approach:	They propose a framework that reconceptualizes discrete reasoning steps as a continuous probabilistic flow, quantifying the contribution of each step toward the ground-truth answer.
Outcome:	The proposed framework achieves a superior balance between inference efficiency and reasoning performance on challenging benchmarks.

Copied to clipboard

Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Zhengtao Yu, Tong Xiao

Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)

Challenge:	Reinforcement learning (RL) training typically improves single-sample success rates but limited exploration of diverse reasoning trajectories.
Approach:	They propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL) they propose 'Steering Probability Squeezing' to enhance exploration without external supervision .
Outcome:	The proposed training paradigm improves Pass@k and improves exploration of diverse reasoning trajectories without external supervision.

Copied to clipboard

Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, Donghong Ji

Text-like Encoding of Collaborative Information in Large Language Models for Recommendation (2024.acl-long)

Challenge:	Existing methods for multimodal sentiment analysis are often dynamically incomplete.
Approach:	They propose a new uncertainty-calibrated elastic alignment framework to address these issues by employing probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment.
Outcome:	The proposed framework outperforms state-of-the-art models in multiple benchmarks and consistently outperformed existing models.

Copied to clipboard

Yang Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Fuli Feng, Xiangnan He

PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)

Challenge:	Existing methods to adapt Large Language Models for Recommendation (LLMRec) do not represent collaborative information in a text-like format, which may not align optimally with LLMs.
Approach:	They propose a novel LLMRec method that integrates collaborative information through text-like encoding.
Outcome:	Extensive experiments show that BinLLM integrates collaborative information better with LLMs.

Copied to clipboard

Yichen He, Guanhua Huang, Peiyuan Feng, Yuan Lin, Yuchen Zhang, Hang Li, Weinan E

Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation (2025.findings-acl)

Challenge:	We introduce PaSa, an advanced Paper Search agent powered by large language models . despite being trained on synthetic data, PaSA outperforms existing baselines on RealScholarQuery .
Approach:	They introduce PaSa, an advanced Paper Search agent powered by large language models . they optimize PaSA using a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained queries .
Outcome:	The paper analyzes the performance of a paper search agent using a synthetic dataset . it significantly outperforms existing benchmarks on RealScholarQuery .

Copied to clipboard

Keqin Bao, Ming Yan, Yang Zhang, Jizhi Zhang, Wenjie Wang, Fuli Feng, Xiangnan He

Low-Resource NLP

Challenge:	Existing Large Language Model (LLM)-based recommender systems face challenges to adapt to dynamic user interests without any model-level updates.
Approach:	They propose a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats.
Outcome:	The proposed model adapts to dynamic user interests without model updates without any model updates and is available online at https://anonymous.4open.science/r/RecICL-8003.

DivScore: Zero-Shot Detection of LLM-Generated Text in Specialized Domains (2025.emnlp-main)

Copied to clipboard

Zhihui Chen, Kai He, Yucheng Huang, Yunxiao Zhu, Mengling Feng

Mixup Decoding for Diverse Machine Translation (2021.findings-emnlp)

Challenge:	Existing zero-shot detectors fail when applied to specialized content due to domain shift . DivScore outperforms state-of-the-art detectors in specialized domains .
Approach:	They propose a zero-shot detection framework that uses normalized entropy-based scoring and domain knowledge distillation to identify LLM-generated text in specialized domains.
Outcome:	The proposed framework outperforms state-of-the-art detectors on medical and legal datasets with 14.4% higher AUROC and 64.0% higher recall.

Copied to clipboard

Jicheng Li, Pengzhi Gao, Xuanfu Wu, Yang Feng, Zhongjun He, Hua Wu, Haifeng Wang

Machine Translation

Challenge:	Existing methods for generating multiple translations for source and target languages neglect the one-to-many mapping between the source and the target languages.
Approach:	They propose a method to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding.
Outcome:	Experiments on WMT’16 en-ro, WMT'14 en de, and WMT ‘17 zh-en show that the proposed method outperforms all previous diverse machine translation methods.

Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission (2024.findings-naacl)

Copied to clipboard

Jianfeng He, Linlin Yu, Shuo Lei, Chang-Tien Lu, Feng Chen

Information Extraction & Text Mining

Challenge:	Named entity recognition tasks are often suboptimal for NER . previous work focused on UE-NER, which estimates uncertainty scores for ner .
Approach:	They propose to use a Sequential Labeling Posterior Network to estimate uncertainty for NER . they propose to consider wrong-span cases and to evaluate the specificity of wrong-pan cases.
Outcome:	The proposed system improves on three datasets and AUPR on MIT-Restaurant datasets.

GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)

Copied to clipboard

Hao Fang, Yuejie Zhang, Rui Feng, Yingwen Wang, Qing Wang, Wen He, Xiaobo Zhang, Tao Zhang, Shang Gao

Information Extraction & Text Mining

Challenge:	Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations .
Approach:	a framework that reduces the problem space using a graph-enhanced Transformer-based model is proposed . GLiM leverages large language models for reasoning to reduce the problem-space .
Outcome:	GLiM boosts average recall and F1 scores on biomedical datasets . compared with existing models, GLim outperforms existing models on biomedicine benchmarks compared to existing models .

Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)

Copied to clipboard

Xun Deng, Wenjie Wang, Fuli Feng, Hanwang Zhang, Xiangnan He, Yong Liao

Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)

Challenge:	Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples.
Approach:	They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases.
Outcome:	The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance.

Copied to clipboard

Jiayi Zeng, Yizhe Feng, Mengliang He, Wenhui Lei, Wei Zhang, Zeming Liu, Xiaoming Shi, Aimin Zhou

Multimodal Neural Machine Translation: A Survey of the State of the Art (2025.emnlp-main)

Challenge:	Current error-handling works are performed in a passive manner, with explicit error- handling instructions.
Approach:	They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research.
Outcome:	The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances.

Copied to clipboard

Yi Feng, Chuanyi Li, Jiatong He, Zhenyu Hou, Vincent Ng

Multilinguality

Challenge:	Multimodal neural machine translation (MNMT) is a task that aims to translate text into the target language using neural networks.
Approach:	They propose to integrate other modalities with textual data to enhance translation performance.
Outcome:	The proposed task aims to integrate visual modality with textual data to improve translation quality.

Empowering Language Understanding with Counterfactual Reasoning (2021.findings-acl)

Copied to clipboard

Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

Reasoning

Challenge:	Existing methods for language understanding use the recognized patterns in the testing phase that are inherently different from us humans who have counterfactual thinking.
Approach:	They propose a counterfactual Reasoning Model which mimics counterfactive thinking by learning from few counterffact samples.
Outcome:	The proposed model can detect and make predictions from textual patterns . it can also detect negative sarcastic puns by comparing them with imaginations .

A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base (2021.findings-emnlp)

Copied to clipboard

Yu Feng, Jing Zhang, Gaole He, Wayne Xin Zhao, Lemao Liu, Quan Liu, Cuiping Li, Hong Chen

AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)

Challenge:	Existing knowledge bases (KBs) can explicitly facilitate the QA process.
Approach:	They propose a numerical reasoning model pretraining NumGNN and NumTransformer, guided by explicit self-supervision signals, to enhance numerical reasoning ability for IR-based KBQA models.
Outcome:	Extensive experiments on two KBQA benchmarks confirm the effectiveness of the proposed model.

Copied to clipboard

Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui

InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care (2025.emnlp-demos)

Challenge:	Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge.
Approach:	They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions.
Outcome:	The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks.

Copied to clipboard

Kai He, Qika Lin, Hao Fei, Eng Siong Chng, Dehan Hong, Marcus Eng Hock Ong, Mengling Feng

Green & Sustainable NLP

Challenge:	Existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triages rates.
Approach:	They propose to use an AI-driven multilingual TT system to provide decision support for triage.
Outcome:	The proposed system achieves word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction.

Large Language Models are Complex Table Parsers (2023.emnlp-main)

Copied to clipboard

Bowen Zhao, Changkai Ji, Yuejie Zhang, Wen He, Yingwen Wang, Qing Wang, Rui Feng, Xiaobo Zhang

Minimal, Local, and Robust: Embedding-Only Edits for Implicit Bias in T2I Models (2025.emnlp-main)

Challenge:	Extensive experiments and results on Complex Table QA datasets, i.e., the open-domain dataset HiTAB and the aviation domain dataset AIT-QA show that our approach significantly outperforms previous work on both datasets.
Approach:	They propose to incorporate Generative Pre-trained Transformer 3.5 to address the specific challenges posed by Complex Table QA by reconstructing tables into tuples and using prompt templates to create dialogues.
Outcome:	The proposed approach outperforms previous work on complex table parsing datasets and leads to state-of-the-art (SOTA) performance.

Copied to clipboard

Feng He, Chao Zhang, Zhixue Zhao

Improving Entity Linking through Semantic Reinforced Entity Embeddings (2020.acl-main)

Challenge:	EmbEdit is a text-to-image editing method that only fine-tunes the word token embedding (WTE) of the target object.
Approach:	They propose a method to edit implicit assumptions and priors in text-to-image models without affecting unrelated objects or degrading overall performance.
Outcome:	The proposed method outperforms previous methods in various models, tasks, and editing scenarios.

Copied to clipboard

Feng Hou, Ruili Wang, Jun He, Yi Zhou

Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)

Challenge:	Existing entity embeddings are effective, but too distinctive for linking models to learn contextual commonality.
Approach:	They propose a method to inject fine-grained semantic information into entity embeddings . they use word embedds of type words to generate semantic embeddngs based on existing embeddables a sample of semantic information is injected into the embedded entities .
Outcome:	The proposed method reduces the distinctiveness of existing embeddings and improves performance.

Copied to clipboard

Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bin Feng, Yuan Yao, Yu Li