Papers by Yao He

TensorOpera Router: A Multi-Model Router for Efficient LLM Inference (2024.emnlp-industry)

Challenge:	Existing evaluation frameworks focus on isolated question-answering tasks that may not capture the essential aspects of strategic reasoning.
Approach:	They evaluate 13 large language models across over 800 games in chess . they use a chessian-based framework to test strategic reasoning and pattern recognition .
Outcome:	The proposed framework improves performance and basic understanding of large language models.

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Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang, Salman Avestimehr, Chaoyang He

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable performance across a diverse set of domain-specific tasks.
Approach:	They propose a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements.
Outcome:	The proposed model improves query efficiency by 40% and costs by 30% while maintaining or enhancing model performance by 10%.

Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)

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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu

Structured Data in NLP

Challenge:	Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions.
Approach:	They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models.
Outcome:	The proposed method outperforms existing methods on two datasets.

Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL (2026.findings-acl)

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Zhewei Yao, Guoheng Sun, Łukasz Borchmann, Zheyu Shen, Minghang Deng, Bohan Zhai, Hao Zhang, Ang Li, Yuxiong He

Programming Languages in NLP

Challenge:	Translating natural language questions into SQL is a core challenge in natural language understanding and human-computer interaction.
Approach:	They propose a reinforcement learning framework and model family to generate accurate, executable SQL using a lightweight reward signal based solely on execution correctness.
Outcome:	The proposed framework outperforms previous versions of 70B-class systems and achieves state-of-the-art execution accuracy across six diverse Text2SQL benchmarks.

Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs (2023.findings-emnlp)

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Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao

Semantic Text Processing

Challenge:	Complex Query Answering (CQA) is a challenge task of Knowledge Graphs due to incompleteness of KGs.
Approach:	They propose a query embedding approach that decouples the training for simple and complex queries.
Outcome:	The proposed approach decouples training for simple and complex queries and achieves state-of-the-art performance over three public benchmarks.

Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)

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Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, Guodong Zhou

Multilinguality

Challenge:	Experimental results show that cross-language data expansion results in performance degradation.
Approach:	They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus.
Outcome:	The proposed method improves ED performance by 1.6% over the straight data combination.

GRAD: Generalizing RAG Adaptation with Decoding (2026.acl-long)

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Youngwon Lee, Seung-won Hwang, Zhewei Yao, Yuxiong He

Information Retrieval

Challenge:	Using GRAD, we can steer Retrieval-augmented generation objectives without retraining large language models.
Approach:	They propose an adaptive decoding-time framework that keeps the base generator fixed and composes small, objective-specific guidance at inference.
Outcome:	The proposed framework improves accuracy with favorable latency across public benchmarks and private settings with no in-domain labels while reliably activating helpful objectives and suppressing harmful ones, adaptively to tasks.

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis (2025.findings-naacl)

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Zeao Tu, Xiangdi Meng, Yu He, Zihan Yao, Tianyu Qi, Jun Liu, Ming Li

OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization (2025.acl-long)

Challenge:	Existing methods for enhancing large language models lack clear metrics for evaluating data characteristics.
Approach:	They propose a method that integrates models, data, and tasks to refine datasets.
Outcome:	The proposed method achieves comparable results to full-scale fine-tuning using only half the data in mathematical tasks and exhibits strong generalization across different models and domains.

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Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Hongming Zhang, Tianqing Fang, Zhenzhong Lan, Dong Yu

MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation (2023.emnlp-main)

Challenge:	Existing studies focus on building text-only agents in synthetic environments where the reward signals are clearly defined.
Approach:	They propose a multimodal web agent that can autonomously conduct real-world exploration and improve itself after each iteration.
Outcome:	The proposed agent improves itself after each iteration, demonstrating strong performance across multiple test sets.

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Zexue He, Yu Wang, An Yan, Yao Liu, Eric Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu

TAGQuant: Token-Aware Clustering for Group-Wise Quantization (2026.eacl-industry)

Challenge:	Existing medical datasets require high quality domain-specific datasets.
Approach:	They propose a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare.
Outcome:	The proposed model provides granular potential usage and supports a wide range of tasks.

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Jaeseong Lee, Seung-won Hwang, Aurick Qiao, Zhewei Yao, Yuxiong He

Text Clustering

Challenge:	Existing work clusters channels using token dimension, which is suboptimal for grouping . a common challenge in LLM quantization is supporting "group-wise" quantization .
Approach:	They propose a method to group channels with similar activation distributions using tokens . they propose shuffle operation that reduces relative GSM8K error by 86% .
Outcome:	The proposed method reduces GSM8K error by 86% in both INT4 and MXFP4 formats compared to baselines .

Word-level Commonsense Knowledge Selection for Event Detection (2024.lrec-main)

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Shuai Yang, Yu Hong, Shiming He, Qingting Xu, Jianmin Yao

Dialogue Systems & Conversational Agents

Challenge:	Event Detection (ED) is a task of automatically extracting multi-class trigger words . Xie and Tu, 2022, use a Context-specific Knowledge Selector to select commonsense knowledge of words based on living contexts .
Approach:	They use a Context-specific Knowledge Selector to select the exact commonsense knowledge of words from a large knowledge base.
Outcome:	The proposed approach achieves the F1-score of about 78.3% on the ACE-2005 dataset.

Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO (2026.acl-long)

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Jinquan Zheng, Jia Yuan, Jiacheng Yao, Chenyang Gu, Pujun Zheng, Guoxiu He

R3-SQL: Ranking Reward and Resampling for Text-to-SQL (2026.findings-acl)

Challenge:	Existing inference-time debiasing ignores that the same question should yield consistent answers across permutations.
Approach:	They propose a permutation-aware group-relative policy optimization which enforces permutations-consistent semantic reasoning.
Outcome:	The proposed model outperforms strong baselines across seven benchmarks while maintaining high overall performance.

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Hojae Han, Yeonseok Jeong, Seung-won Hwang, Zhewei Yao, Yuxiong He

Programming Languages in NLP

Challenge:	Existing rankers assign inconsistent scores to functionally equivalent SQL queries . ranking cannot recover when the correct SQL is absent from the pool.
Approach:	They propose a Text-to-SQL framework that rewards ranking and resampling . it first groups candidates by execution result and ranks groups for consistency .
Outcome:	The proposed framework achieves 75.03 execution accuracy on BIRD-dev, a new state of the art among methods using models with disclosed sizes.

LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)

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Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu

Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)

Challenge:	Structured knowledge grounding (SKG) tasks are a key part of many NLP applications.
Approach:	They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format .
Outcome:	The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning.

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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang

EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)

Challenge:	Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point.
Approach:	They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully.
Outcome:	The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations.

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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji

Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)

Challenge:	Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments.
Approach:	They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals.
Outcome:	The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels.

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Huanxuan Liao, Shizhu He, Yao Xu, Yuanzhe Zhang, Shengping Liu, Kang Liu, Jun Zhao

WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models (2024.acl-long)

Challenge:	Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened.
Approach:	They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context.
Outcome:	The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps.

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Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, Dong Yu

CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation (2025.naacl-short)

Challenge:	Existing web agents only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios.
Approach:	They propose a large multimodal model-powered web agent that can complete user instructions end-to-end by interacting with real-world websites.
Outcome:	The proposed agent achieves 59.1% task success rate, surpassing both GPT-4 and WebVoyager setups.

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Youngwon Lee, Seung-won Hwang, Daniel F Campos, Filip Graliński, Zhewei Yao, Yuxiong He

SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)

Challenge:	Existing methods to ground large language models fail to adequately attend to all contexts . position bias is hindered by retrieval-augmented generation, which requires constant attention .
Approach:	They propose to augment and distill training instances with their perturbed positions to encourage consistent predictions . they also propose to balance COnsistency and Rank Distillation by combining noise-controlled perturbations with augmentation and distillation.
Outcome:	The proposed method outperforms existing methods in diverse RAG benchmarks.

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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li

Challenge:	Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy.
Approach:	They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration.
Outcome:	The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent .

Optimizing Reasoning for Text-to-SQL with Execution Feedback (2025.findings-acl)

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Bohan Zhai, Canwen Xu, Yuxiong He, Zhewei Yao

Programming Languages in NLP

Challenge:	Large language models excel in many reasoning tasks, but their ability to leverage Chain-of-Thought (CoT) reasoning remains underexplored.
Approach:	They propose a framework that iteratively optimizes open-source LLMs by combining CoT reasoning with off-policy and on-poly DPO, relying solely on execution accuracy as feedback.
Outcome:	The proposed framework improves execution accuracy on BIRD and Spider datasets.

Cut the Deadwood Out: Backdoor Purification via Guided Module Substitution (2025.findings-emnlp)

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Yao Tong, Weijun Li, Xuanli He, Haolan Zhan, Qiongkai Xu

Robustness in NLP

Challenge:	Model NLP models are often trained on datasets from untrusted platforms, posing significant risks of data poisoning attacks.
Approach:	They propose a retraining-free method that selectively replaces modules in the victim model based on a trade-off signal between utility and backdoor.
Outcome:	The proposed method outperforms even the strongest defense baseline against challenging attacks like LWS.

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

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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.

FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation (2025.acl-demo)

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Zheqi He, Yesheng Liu, Jing-Shu Zheng, Xuejing Li, Jin-Ge Yao, Bowen Qin, Richeng Xuan, Xi Yang

Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026.acl-long)

Challenge:	FlagEvalMM is an evaluation framework designed to assess multimodal models . it is designed to be used for vision-language understanding and generation tasks .
Approach:	They propose an evaluation framework that decouples model inference from evaluation through an independent evaluation service.
Outcome:	The evaluation framework offers accurate and efficient insights into model strengths and limitations.

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Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang

AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)

Challenge:	Recent research shows that LLM Agents can generate “believable” human behaviors via prompt-only methods, leaving open questions of whether they can accurately generate step-by-step actions in multi-turn interaction tasks.
Approach:	They propose to use shopping data to evaluate LLMs' ability to accurately generate step-by-step actions in a multi-turn interaction task.
Outcome:	The proposed model achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing improvements of 5.4% and 13.85% over baselines.

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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp

Multilinguality

Challenge:	Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages.
Approach:	They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines.
Outcome:	The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR.

Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)

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Jing Zhou, Peng Wang, Wenjun Ke, Jiajun Liu, Yao He

Demonstration Retrieval-Augmented Generative Event Argument Extraction (2024.lrec-main)

Challenge:	Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks .
Approach:	They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks .
Outcome:	The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability.

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Shiming He, Yu Hong, Shuai Yang, Jianmin Yao, Guodong Zhou

Low-Resource NLP

Challenge:	Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets.
Approach:	They propose a generative EAE that uses event knowledge-injected generator and demonstration retriever to generate event arguments from training data.
Outcome:	The proposed method outperforms baselines and can be generalized to various datasets.

MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension (2024.findings-emnlp)

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Xingyu Lu, He Cao, Zijing Liu, Shengyuan Bai, Leqing Chen, Yuan Yao, Hai-Tao Zheng, Yu Li

Question Answering

Challenge:	Existing models generate erroneous information and evaluations fail to assess factual correctness of models.
Approach:	They propose to use MoleculeQA to evaluate molecular factual correctness in large language models by organizing molecules into a taxonomy and building QA pairs through human and LLM efforts.
Outcome:	The proposed model improves the factual correctness of generated information and enables the development of new models.

GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)

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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao

Cognitive Bias in Decision-Making with LLMs (2024.findings-emnlp)

Challenge:	Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning.
Approach:	They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis.
Outcome:	The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection.

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Jessica Echterhoff, Yao Liu, Abeer Alessa, Julian McAuley, Zexue He

Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)

Challenge:	Large language models inherit societal biases against protected groups and can be subject to functionally resembling cognitive bias.
Approach:	They propose a framework to uncover, evaluate, and mitigate cognitive bias in large language models by using a dataset containing 13,465 prompts to evaluate LLM decisions on different cognitive biases.
Outcome:	The proposed framework uncovers, evaluates, and mitigates cognitive bias in large language models, particularly in high-stakes decision-making tasks.

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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang

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

Challenge:	Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated.
Approach:	They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities.
Outcome:	The proposed benchmark features 4,761 diverse image sequences with varying lengths.

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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

Scaling Vision-Language Models with Sparse Mixture of Experts (2023.findings-emnlp)

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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Sheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell, Kurt Keutzer, Yuxiong He

Semantic Text Processing

Challenge:	a study explores the effectiveness of mixture-of-experts (MoE) techniques in scaling vision-language models . alayrac and colleagues demonstrate the effectiveness and performance of MoE in scaling VLMs .
Approach:	They propose to use sparsely-gated mixture-of-experts techniques to scale vision-language models . they show that MoE can achieve state-of the-art performance over dense models a range of benchmarks .
Outcome:	The proposed approach achieves state-of-the-art performance over dense models of equivalent computational cost.

Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System (2026.acl-long)

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Yifei Wei, Linqing Zhong, Yi Liu, Yuxiang Lu, Xindong He, Maoqing Yao, Guanghui Ren

Challenge:	Vision-Language-Action models ground high-level semantic instructions into executable physical actions.
Approach:	They propose a Coarse-to-Fine Dual-System VLA architecture that decouples learning complexity into a coarse-to fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy.
Outcome:	The proposed architecture decouples learning complexity into a coarse-to-fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy.

FlagEval-Arena: A Side-by-Side Comparative Evaluation Platform for Large Language Models and Text-Driven AIGC (2025.acl-demo)

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Jing-Shu Zheng, Richeng Xuan, Bowen Qin, Zheqi He, Tongshuai.ren Tongshuai.ren, Xuejing Li, Jin-Ge Yao, Xi Yang

Inference Scaling for Bridging Retrieval and Augmented Generation (2025.findings-naacl)

Challenge:	a new evaluation platform for large language models and text-driven AIGCs is available for free.
Approach:	They propose an evaluation platform for side-by-side comparisons of large language models and text-driven AIGC systems.
Outcome:	a new evaluation platform for large language models and text-driven AIGC systems is available for free . the platform is more focused on the Chinese language and more models developed by Chinese institutes .

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Youngwon Lee, Seung-won Hwang, Daniel F Campos, Filip Graliński, Zhewei Yao, Yuxiong He

Information Retrieval

Challenge:	Existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome.
Approach:	They propose to use inference scaling to aggregate inference calls from the permuted order of retrieved contexts to create a new ranking.
Outcome:	The proposed approach improves ROUGE-L on MS MARCO and EM on HotpotQA benchmarks by 7 points.

SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection (2026.acl-industry)

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Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huai Yuen Khor, Hao Wang, Huan He, Yafei Liu, Ke Ma, Ruqian Shi, Sicheng Zhou, Sijia Yao

Dialogue Systems & Conversational Agents

Challenge:	High-quality data in training proactive dialogue agents is scarce, despite fine-tuning and reinforcement learning . a recent study has shown that the effectiveness of supervised fine-touring is limited by the lack of high-quality, domain-specific training data.
Approach:	They propose a framework for training recruitment proactive dialogue agents using a high-fidelity user simulator and a multi-dimensional evaluation framework based on Chain-of-Intention.
Outcome:	The proposed framework outperforms existing simulator-based data selection strategies in a real-world recruitment scenario.

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

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

MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024.lrec-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.

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Xiang Li, Shizhu He, Jiayu Wu, Zhao Yang, Yao Xu, Yang jun Jun, Haifeng Liu, Kang Liu, Jun Zhao

EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety (2025.emnlp-main)

Challenge:	Current Chain-of-thought Distillation methods hinder CoT reasoning performance . student models are separately distilled from specific reasoning tasks . parameter update of student models severely harms CoT ability on unseen reasoning tasks.
Approach:	They propose a method which distills Chain-of-thought reasoning ability of large language models to much smaller student models.
Outcome:	The proposed method improves the reasoning ability of large language models on 14 datasets.

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Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang

GASim: A Graph-Accelerated Hybrid Framework for Social Simulation (2026.acl-long)

Challenge:	EmoAgent evaluates and mitigates mental health hazards in human-AI interactions, especially for vulnerable human users with psychological disorders.
Approach:	EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions.
Outcome:	EmoAgent evaluates and mitigates mental health hazards in human-AI interactions.

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Xuan Zhou, Yanhui Sun, Hantao Yao, Allen He, Yongdong Zhang, Wu Liu

Taming Text-to-Image Synthesis for Novices: User-centric Prompt Generation via Multi-turn Guidance (2025.emnlp-main)

Challenge:	Large-scale social simulators require high latency due to expensive memory retrieval and sequential ABM execution.
Approach:	They propose a graph-accelerated hybrid multi-agent framework for large-scale social simulations that uses large language models and numerical agent-based models to scale up simulations.
Outcome:	The proposed framework delivers 9.94 speedup over the traditional framework and consumes less than 20% of tokens.

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Yilun Liu, Minggui He, Feiyu Yao, Yuhe Ji, Shimin Tao, Jingzhou Du, Justin Li, Jian Gao, Zhang Li, Hao Yang, Boxing Chen, Osamu Yoshie

Low-Resource NLP

Challenge:	Existing solutions for text-to-image synthesis are sensitive on textual prompts, posing a challenge for novice users.
Approach:	They propose a dialogue-based TIS prompt generation model that emphasizes user experience for novice users.
Outcome:	The proposed model emphasizes user experience for novice users . it improves user-centricity score while maintaining a competitive quality of synthesized images.

HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization (2021.emnlp-main)

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Ye Liu, Jianguo Zhang, Yao Wan, Congying Xia, Lifang He, Philip Yu

Information Extraction & Text Mining

Challenge:	Existing methods for summarizing semantic graph structure from raw text are cumbersome and inefficient for long-text documents.
Approach:	They propose a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization.
Outcome:	The proposed model performs state-of-the-art on single- and multi-document summarization tasks while using less memory and fewer parameters.

PAFT: Prompt-Agnostic Fine-Tuning (2025.emnlp-main)

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Chenxing Wei, Yao Shu, Mingwen Ou, Ying He, Fei Yu

Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance (2025.naacl-long)

Challenge:	Prompt-agnostic fine-tuning (PAFT) improves performance by reducing overfitting to specific prompts.
Approach:	They propose a method that enhances robustness through dynamic prompt variation during training.
Outcome:	The proposed method achieves higher generalization accuracy on unseen prompts than standard methods with similar training efficiency.

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Borui Xu, Yao Chen, Zeyi Wen, Weiguo Liu, Bingsheng He

STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning (2025.acl-long)

Challenge:	Large language models (LLMs) provide superior summarization quality, but their high computational resource requirements limit practical use applications.
Approach:	They evaluate 19 small language models for news summarization across 2,000 news samples . they find that top-performing models achieve comparable results to those of 70B LLMs .
Outcome:	The proposed models achieve comparable results to 70B LLMs while generating more concise summaries.

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Jaeseong Lee, Seung-won Hwang, Aurick Qiao, Daniel F Campos, Zhewei Yao, Yuxiong He

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models (2025.acl-long)

Challenge:	Mixture-of-experts (MoEs) have been adopted for reducing inference costs by sparsely activating experts in large language models (LLMs).
Approach:	They propose a structured-then-unstructured approach outperforming both of structured and unstructured pruning for MoEs.
Outcome:	The proposed approach outperforms both of structured and unstructured pruning, especially for MoEs with hundreds of experts.

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Boheng Sheng, Jiacheng Yao, Meicong Zhang, Guoxiu He

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)

Challenge:	Current methods for improving large language models rely on splitting long contexts into fixed-length chunks, compromising accuracy.
Approach:	They propose a method for dynamically separating and selecting chunks of long context, facilitating a more streamlined input for LLMs.
Outcome:	The proposed approach outperforms baseline methods on single-hop and multi-hop question-answering benchmarks.

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Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang

Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training (2024.eacl-long)

Challenge:	Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data .
Approach:	They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation.
Outcome:	The proposed framework outperforms open-source baselines and is competitive with GPT-5.

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Jianfeng He, Julian Salazar, Kaisheng Yao, Haoqi Li, Jason Cai

Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation (2025.findings-emnlp)

Challenge:	End-to-end (E2E) spoken language understanding models are constrained by the cost of collecting speech-semantics pairs.
Approach:	They propose a model that learns E2E SLU without speech-semantics pairs . they propose cross-modal selective self-training (CMSST) to address imbalance and noise issues .
Outcome:	The proposed model learns E2E SLU without speech-semantics pairs . the proposed model requires the domains of speech-text and text-sensitization to match .

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Zhenglin Hua, Jinghan He, Zijun Yao, Tianxu Han, Haiyun Guo, Yuheng Jia, Junfeng Fang

SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation (2025.emnlp-main)

Challenge:	Existing approaches to address hallucinations in large vision-language models require substantial computational cost and time.
Approach:	They propose to leverage sparse autoencoders to identify semantic directions closely associated with faithfulness or hallucination, extracting more precise and disentangled hallucinian-related representations.
Outcome:	The proposed method outperforms existing decoding approaches while maintaining transferability across different model architectures with negligible additional time overhead.

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Aurick Qiao, Zhewei Yao, Samyam Rajbhandari, Yuxiong He

Exploring Layer Activation Dynamic of CoT via Knowledge Probe (2026.acl-long)

Challenge:	Large Language Models (LLMs) are an integral enabler of enterprise applications such as summarization, retrieval augmented generation, and agentic workflows.
Approach:	They propose a model transformation and distillation procedure that prefills later layers’ KV cache using an earlier layer’s output, allowing prompt tokens to skip those later layers.
Outcome:	The proposed procedure can reduce prefill computation by 25-50% across several LLM families while incurring minimum quality degradation.

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Chuanxin Zhang, Jiajun Liu, Yao He, Wenjun Ke, Peng Wang, Yankun Le, Sirui Liu, Zhaoyu Yang

Information Extraction & Text Mining

Challenge:	Chain-of-thought reasoning has emerged as a crucial paradigm for multi-step reasoning tasks.
Approach:	They propose a multi-stage probing framework that enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution.
Outcome:	The proposed framework enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution.

Prediction and Calibration: Complex Reasoning over Knowledge Graph with Bi-directional Directed Acyclic Graph Neural Network (2023.findings-acl)

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Yao Xu, Shizhu He, Li Cai, Kang Liu, Jun Zhao

Structured Data in NLP

Challenge:	Knowledge graphs (KGs) organize world knowledge as interlinked triples which describe entities and their relationships.
Approach:	They propose a bi-directional Directed Acyclic Graph neural network that splits the reasoning process into prediction and calibration.
Outcome:	The proposed model outperforms previous QE models on FB15k, FB16k-237, and NELL995 on prediction and calibration.

PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes (2024.findings-emnlp)

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He Cao, Yanjun Shao, Zhiyuan Liu, Zijing Liu, Xiangru Tang, Yuan Yao, Yu Li

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific domains.
Approach:	They propose a framework that bridges the molecule-text modality gap by integrating a comprehensive benchmark of pretraining strategies and dataset configurations.
Outcome:	The proposed framework improves multimodal LLMs through cross-modal alignment and multi-graph understanding.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

Flexora: Flexible Low-Rank Adaptation for Large Language Models (2025.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Chenxing Wei, Yao Shu, Ying Tiffany He, Fei Yu

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

Challenge:	Large language models (LLMs) have revolutionized artificial intelligence, but performance on specific tasks is limited by knowledge boundaries.
Approach:	They propose a method that automatically selects the most critical layers for fine-tuning to optimize performance across diverse downstream tasks.
Outcome:	The proposed method outperforms baseline models and natural language tasks.

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

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

Challenge:	Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness.
Approach:	They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance.
Outcome:	Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality.

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

Search-in-Context: Efficient Multi-Hop QA over Long Contexts via Monte Carlo Tree Search with Dynamic KV Retrieval (2025.findings-acl)

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Jiabei Chen, Guang Liu, Shizhu He, Kun Luo, Yao Xu, Jun Zhao, Kang Liu

Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)

Challenge:	Existing approaches to multihop question answering (MHQA) over long contexts are often neglecting explicit reasoning or incurring expensive computational costs due to full-attention mechanisms over long contextuals.
Approach:	They propose a framework that integrates Monte Carlo Tree Search (MCTS) with dynamic key-value retrieval to enable iterative, context-aware reasoning.
Outcome:	The proposed framework integrates Monte Carlo Tree Search (MCTS) with dynamic key-value (KV) retrieval to enable iterative, context-aware reasoning.

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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low

InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery (2025.coling-main)

Challenge:	a paper proposes a data-centric perspective of AI research, focusing on large language models.
Approach:	They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer .
Outcome:	The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods .

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He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, Yu Li