Papers by Huan Chen

ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)

Challenge:	Large Vision-Language Models are hindered by a systemic efficiency barrier known as visual token dominance.
Approach:	They propose a systematic taxonomy of efficiency techniques structured around the inference lifecycle . they examine visual encoding, prefilling, and decoding to understand bottlenecks .
Outcome:	The proposed techniques reveal how upstream decisions dictate downstream bottlenecks . the proposed techniques include hybrid compression and modality-aware decoding .

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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers.
Approach:	They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach.
Outcome:	The proposed method outperforms manual methods and outperfies baselines on Taobao in China.

ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)

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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

Challenge:	Existing infrastructure for efficient agentic data processing and model training remains underdeveloped.
Approach:	They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 .
Outcome:	The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks.

Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)

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Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Gang Chen, Sharad Mehrotra

Refusal-Aware Red Teaming: Exposing Inconsistency in Safety Evaluations (2025.emnlp-main)

Challenge:	Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding.
Approach:	They propose a self-speculative decoding approach for accelerating Large Language Models without an auxiliary model.
Outcome:	The proposed method achieves a speedup of up to 1.99 with no additional neural network training and no extra memory footprint.

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Yongkang Chen, Xiaohu Du, Xiaotian Zou, Chongyang Zhao, Huan Deng, Hu Li, Xiaohui Kuang

Challenge:	Large Language Models (LLMs) require rigorous safety evaluations to be effective.
Approach:	They propose a red teaming framework that detects internal model refusals and contrasts them with judgments from an external safety evaluator to generate test cases that expose such discrepancies.
Outcome:	The proposed framework outperforms existing reinforcement learning-based approaches in generating diverse test cases and achieves a substantially higher discovery rate of refusal gaps.

SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention (2025.acl-long)

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Chengshuai Zhao, Zhen Tan, Chau-Wai Wong, Xinyan Zhao, Tianlong Chen, Huan Liu

LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding (2023.acl-long)

Challenge:	Content analysis is labor-intensive and time-consuming process that requires multiple rounds of manual annotation, domain expert discussion, and rule-based refinement.
Approach:	They propose a multi-agent framework that effectively Simulates Content Analysis via Large language model (LLM) ag Ents.
Outcome:	The proposed framework achieves human-approximated performance across various content analysis tasks.

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Yi Tu, Ya Guo, Huan Chen, Jinyang Tang

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)

Challenge:	Pre-trained models on document images with transformer-based backbones have led to significant performance gains in this field.
Approach:	They propose a multi-modal pre-training model that combines text, layout and image . they propose to use local 1D position instead of global 1D positions as layout input .
Outcome:	The proposed model can achieve state-of-the-art results on a wide variety of VrDU problems.

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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen

Structured Data in NLP

Challenge:	Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains.
Approach:	They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease.
Outcome:	The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs.

Transfer-Aware Data Selection for Domain Adaptation in Text Retrieval (2025.findings-emnlp)

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Linzhu Yu, Huan Li, Ke Chen, Lidan Shou

ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)

Challenge:	Existing methods to improve domain adaptation do not guarantee improved adaptability, but may negatively impact model performance.
Approach:	They propose a framework that can effectively improve model adaptability by selecting beneficial data without evaluating all source data.
Outcome:	The proposed framework improves model adaptability by selecting beneficial data without evaluating all source data.

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Yuan Chen, Zhenyu Hu, Mengge Xue, Cao Te, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

T2DR: A Two-Tier Deficiency-Resistant Framework for Incomplete Multimodal Learning (2025.findings-acl)

Challenge:	Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent.
Approach:	They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency.
Outcome:	The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video.

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Han Lin, Xiu Tang, Huan Li, Wenxue Cao, Sai Wu, Chang Yao, Lidan Shou, Gang Chen

BOSE: A Systematic Evaluation Method Optimized for Base Models (2025.findings-acl)

Challenge:	Existing incomplete multimodal learning frameworks are inadequate for integrating multimodal data.
Approach:	They propose a framework for incomplete multimodal learning that is deficiency-resistant and provides two modules to address fine-grained deficiencies.
Outcome:	The proposed framework outperforms the SOTA models on two well-known multimodal benchmarks.

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Hongzhi Luan, Changxin Tian, Zhaoxin Huan, Xiaolu Zhang, Kunlong Chen, Zhiqiang Zhang, Jun Zhou

Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)

Challenge:	Existing evaluation methods for large language models (LLMs) are inadequate to provide solid conclusions for key experiments such as data ablation and scaling law.
Approach:	They propose a method specifically designed to optimize the evaluation of base models by incorporating two innovations: In-Context Light-instruction Prompt and Blank-ppl for multi-choice tasks with candidate options.
Outcome:	The proposed method significantly improves stability and consistency of evaluations during pre-training and consistency between base and instruct models.

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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu

Dialogue Systems & Conversational Agents

Challenge:	Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation.
Approach:	They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor.
Outcome:	The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets.

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025.acl-long)

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Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Rajan Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister

Natural Language Interfaces

Challenge:	Existing approaches to long-term dialogue memory management fail to capture the natural semantic structure of conversations, leading to fragmented and incomplete representations.
Approach:	They propose a mechanism that integrates forward- and backward-looking reflections into a personalized memory bank for effective future retrieval.
Outcome:	The proposed mechanism outperforms state-of-the-art benchmarks on a long-term dialogue memory model.

An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots (2021.naacl-industry)

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Shuangyong Song, Chao Wang, Haiqing Chen, Huan Chen

Natural Language Interfaces

Challenge:	E-commerce has grown rapidly over the last several years, and chatbots for intelligent customer service are simultaneously drawing attention.
Approach:	They propose a framework to obtain proper answer to customers’ emotional questions using emotion classification model and text matching.
Outcome:	The proposed framework is very promising on real online systems.

Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction (2023.emnlp-main)

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Chong Zhang, Ya Guo, Yi Tu, Huan Chen, Jinyang Tang, Huijia Zhu, Qi Zhang, Tao Gui

One Pair Suffices: Unlocking Universal Zero-Shot Translation via Cross-Architecture Alignment (2026.acl-long)

Challenge:	Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs).
Approach:	They propose a method to predict token sequences within visually-rich documents by a simple prediction head.
Outcome:	The proposed method can be used to predict token mentions as token sequences within documents.

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Hao Zong, Cong Hu Yuan, Chao Bei, Wentao Chen, Huan Liu, Kaiyu Huang, Degen Huang

HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference (2026.acl-long)

Challenge:	Current paradigms for empowering Large Language Models with multilingual capabilities rely heavily on massive instruction tuning.
Approach:	They propose a hybrid cross-alignment approach that fuses a frozen NLLB encoder with a Qwen decoder via a closed-loop dual-adapter architecture.
Outcome:	The proposed model outperforms towerPlus-9B and Aya-101 on language-agnostic projection protocols.

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Bowen Zeng, Feiyang Ren, Jun Zhang, Xiaoling Gu, Ke Chen, Lidan Shou, Huan Li

When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (2024.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) are hindered by the rapid growth of key–value (KV) caches.
Approach:	They propose a hybrid KV cache compression framework that reduces KV memory by 7.9 and speeds up decoding by 1.52.
Outcome:	Experiments on 11 multimodal benchmarks show that HYBRIDKV cuts KV cache memory by 7.9 and speeds up decoding by 1.52.

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Ziru Chen, Michael White, Ray Mooney, Ali Payani, Yu Su, Huan Sun

Information Retrieval

Challenge:	Existing methods to build language agents that can plan efficiently and accurately have not met the needs of advanced planning methods to achieve such improvements.
Approach:	They propose to use iterative correction and tree search to solve multi-step problems in a language agent framework with three components: a generator, a discriminator, and a planning method.
Outcome:	The proposed methods improve performance on two tasks, text-to-SQL parsing and mathematical reasoning, while using discriminators with 90% accuracy.

SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning (2025.emnlp-main)

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Yicheng Ji, Jun Zhang, Heming Xia, Jinpeng Chen, Lidan Shou, Gang Chen, Huan Li

Tooling or Not Tooling? The Impact of Tools on Language Agents for Chemistry Problem Solving (2025.findings-naacl)

Challenge:	Video large language models (Vid-LLMs) rely on dense video token representations and require substantial memory and computational overhead in both prefilling and decoding.
Approach:	They propose a training-free speculative decoding framework that prunes up to 90% of video tokens to enable efficient speculation without sacrificing accuracy.
Outcome:	The proposed framework achieves 2.68 speedup on LLaVA-OneVision-72B and 2.11 speed up on Qwen2.5-VL-32B.

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Botao Yu, Frazier N. Baker, Ziru Chen, Garrett Herb, Boyu Gou, Daniel Adu-Ampratwum, Xia Ning, Huan Sun

Challenge:	Existing evaluations of large language models (LLMs) with tools are limited and qualitative . existing evaluations have been limited and only focus on 14 tasks focusing on compound synthesis.
Approach:	They propose to develop an enhanced chemistry agent over ChemCrow to improve chemistry problem solving by integrating tools into LLMs.
Outcome:	The proposed agent does not consistently outperform its base LLMs without tools on specialized chemistry tasks and general chemistry questions.

Contextualization Distillation from Large Language Model for Knowledge Graph Completion (2024.findings-eacl)

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Dawei Li, Zhen Tan, Tianlong Chen, Huan Liu

From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)

Challenge:	Existing knowledge graph completion models lack textual information, which limits their performance . a plug-in-and-play approach is needed to train small models in descriptive context .
Approach:	They propose a plug-in-and-play approach to knowledge graph completion that prompts LLMs to generate descriptive context.
Outcome:	The proposed method improves performance on Wikipedia articles and synset definitions.

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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu

Challenge:	Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios .
Approach:	They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios.
Outcome:	The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm.

Geo-BERT Pre-training Model for Query Rewriting in POI Search (2021.findings-emnlp)

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Xiao Liu, Juan Hu, Qi Shen, Huan Chen

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark (2025.acl-long)

Challenge:	Existing methods to solve the word mismatch between queries and documents are often inadequate to integrate geographic information into the pre-training model.
Approach:	They propose to train a pre-training model to integrate semantics and geographic information in the pre-trained representations of POIs.
Outcome:	The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services.

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Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig

Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)

Challenge:	Recent advances in multimodal large language models have led to progress in tackling complex reasoning tasks that combine textual and visual information.
Approach:	They introduce a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark.
Outcome:	The proposed model performs lower on MMMU-Pro than on the previous benchmark, ranging from 16.8% to 26.9%.

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Yuhan Wu, Huan Zhang, Wei Cheng, Chen Shen, Jingyue Yang, Wei Hu

Multilinguality

Challenge:	Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available.
Approach:	They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning.
Outcome:	The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training.

How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities (2024.naacl-long)

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Lingbo Mo, Boshi Wang, Muhao Chen, Huan Sun

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

Challenge:	Rapid progress in open-source Large Language Models (LLMs) is driving AI development, but lacks sufficient trustworthiness to detect and mitigate adversarial demonstrations.
Approach:	They propose an extended Chain of Utterances-based (CoU) prompting strategy to attack open-source LLMs.
Outcome:	The proposed attack strategy is based on malicious demonstrations and toxicity tests on open-source models.

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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

Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model (2024.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 .

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Chuhao Jin, Kening Ren, Lingzhen Kong, Xiting Wang, Ruihua Song, Huan Chen

Dialogue Systems & Conversational Agents

Challenge:	Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuating users.
Approach:	They propose a general method to learn a persuasive model based on LLMs through intent-to-strategy reasoning, which summarizes the intent of user’s utterance and reasons next strategy to respond.
Outcome:	The proposed method outperforms baselines on automatic evaluation metric Win-Rate and human evaluation on two datasets.

Glue pizza and eat rocks - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models (2024.emnlp-main)

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Zhen Tan, Chengshuai Zhao, Raha Moraffah, Yifan Li, Song Wang, Jundong Li, Tianlong Chen, Huan Liu

Text-to-SQL Error Correction with Language Models of Code (2023.acl-short)

Challenge:	Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases.
Approach:	They propose to exploit openness of RAG models by injecting deceptive content into the retrieval database, intentionally changing the model’s behavior.
Outcome:	The proposed model can be exploited through crafted content uploads with access to the retriever.

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Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun

Programming Languages in NLP

Challenge:	Existing semantic parsers are not accurate enough for use in text-to-SQL parsing tasks.
Approach:	They propose to build clause-level edit models to correct SQL queries instead of token-level ones.
Outcome:	The proposed model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines.

Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding (2024.emnlp-main)

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Chong Zhang, Yi Tu, Yixi Zhao, Chenshu Yuan, Huan Chen, Yue Zhang, Mingxu Chai, Ya Guo, Huijia Zhu, Qi Zhang, Tao Gui

Exploring Chain of Thought Style Prompting for Text-to-SQL (2023.emnlp-main)

Challenge:	Existing models of layout reading order do not convey the complete reading order information in the layout.
Approach:	They propose to model layout reading order as ordering relations over layout elements . they propose a reading-order-relation-enhancing pipeline to improve model performance .
Outcome:	The proposed model outperforms existing models on a visual-rich document dataset and on eight cross-domain VrD-IE/QA tasks without targeted optimization.

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Chang-Yu Tai, Ziru Chen, Tianshu Zhang, Xiang Deng, Huan Sun

Programming Languages in NLP

Challenge:	In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks.
Approach:	They propose a new chain of thought prompting method that enhances LLMs’ reasoning ability through chain of thinking prompting, including the original chain-of-thought prompting and least-to-most prompting.
Outcome:	The proposed method brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gain, versus the least-to-most prompting.

Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again (2022.findings-emnlp)

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Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, You Chen, Lang Li, Huan Sun, Yu Su

Low-Resource NLP

Challenge:	Large pre-trained language models (PLMs) such as GPT-3 have shown strong in-context learning capabilities, which are appealing for domains such as biomedicine that feature high and diverse demands of language technologies but also high data annotation costs.
Approach:	They propose to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two representative biomedical information extraction tasks: named entity recognition and relation extraction.
Outcome:	The proposed model underperforms on two representative biomedical information extraction tasks.

MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models (2025.emnlp-demos)

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Zhiwei Liu, Jielin Qiu, Shiyu Wang, Jianguo Zhang, Zuxin Liu, Roshan Ram, Haolin Chen, Weiran Yao, Shelby Heinecke, Silvio Savarese, Huan Wang, Caiming Xiong

Challenge:	Existing evaluation frameworks suffer from limitations such as static task benchmarks, limited scope, and inadequate integration with practical applications.
Approach:	They propose an open-source, Model Context Protocol-based evaluation framework specifically tailored for comprehensive and systematic assessment of LLM-powered agents.
Outcome:	The proposed framework uncovers nuanced performance patterns and identify domain-specific strengths and weaknesses, providing valuable insights beyond traditional binary success metrics.

Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)

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Xiang Yue, Boshi Wang, Ziru Chen, Kai Zhang, Yu Su, Huan Sun

Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning (P18-1)

Challenge:	Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem.
Approach:	They investigate automatic evaluation of attribution given by large language models . they define different types of attributed errors and then explore two approaches .
Outcome:	The proposed methods highlight promising signals and challenges.

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Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh

Sharpness-Aware Minimization with Dynamic Reweighting (2022.findings-emnlp)

Challenge:	Visual language grounding is widely studied in modern neural image captioning systems . a novel algorithm for crafting adversarial examples in image captions is proposed .
Approach:	They propose an algorithm to craft adversarial examples in machine vision and perception . their approach provides two evaluation approaches to check if they can mislead systems .
Outcome:	The proposed algorithm can craft visually-similar adversarial examples with randomly targeted captions or keywords, and the results are transferable to other image captioning systems.

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Wenxuan Zhou, Fangyu Liu, Huan Zhang, Muhao Chen

AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)

Challenge:	Deep neural networks are often overparameterized and can overfit training data.
Approach:	They propose an adversarial weight minimization algorithm that conducts adversarials and finds a common adversaria per-batch.
Outcome:	The proposed algorithm finds a common adversarial weight perturbation per-batch.

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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun

xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)

Challenge:	AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery.
Approach:	They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows.
Outcome:	The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages.

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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

Multimodal Large Language Models for Multi-Subject In-Context Image Generation (2026.acl-long)

Challenge:	Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks.
Approach:	They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance.
Outcome:	The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks.

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Yucheng Zhou, Dubing Chen, Huan Zheng, Jianbing Shen

SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)

Challenge:	Recent advances in text-to-image (T2I) generation have enabled visually coherent image synthesis from descriptions, but generating images containing multiple given subjects remains challenging.
Approach:	They propose a model that enables automatic and scalable data generation without manual annotations to overcome the data scarcity.
Outcome:	The proposed model overcomes the data scarcity and lacks manual annotations.

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Cao Te, Mengge Xue, Zhenyu Hu, Yuan Chen, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)

Challenge:	Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity.
Approach:	They propose a framework for the minimalist rectification of non-compliant image ads.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency.

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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu

Error Detection for Text-to-SQL Semantic Parsing (2023.findings-emnlp)

Challenge:	Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets.
Approach:	They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring.
Outcome:	The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring .

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Shijie Chen, Ziru Chen, Huan Sun, Yu Su

Programming Languages in NLP

Challenge:	Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use.
Approach:	They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries .
Outcome:	The proposed model outperforms parser-dependent uncertainty metrics on three strong parsers . it could improve the performance and usability of text-to-SQL semantic parsing, it is shown .

See the Forest for the Trees: Loosely Speculative Decoding via Visual-Semantic Guidance for Efficient Inference of Video LLMs (2026.acl-long)

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Yicheng Ji, Jun Zhang, Jinpeng Chen, Cong Wang, Lidan Shou, Gang Chen, Huan Li

Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs (2024.findings-emnlp)

Challenge:	Existing methods for video understanding suffer from autoregressive generation of tokens.
Approach:	They propose a training-free loosely SD framework for Video-LLMs that uses visual-relevant tokens to accurately pinpoint the latter.
Outcome:	The proposed framework boosts the accepted length and speedup ratio by 136% and 35% compared to SOTA training-free SD methods for Video-LLMs.

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Kexin Ma, Ruochun Jin, Wang Haotian, Wang Xi, Huan Chen, Yuhua Tang, Qian Wang

Combating Security and Privacy Issues in the Era of Large Language Models (2024.naacl-tutorials)

Challenge:	Existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods.
Approach:	They propose to use Context-Driven Index Trimming framework to capture and regulate consistency between retrieved contexts and modify indexes in the database.
Outcome:	Experiments show that the proposed framework can improve answer quality by 3.75% on open-domain question-answering tasks.

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Muhao Chen, Chaowei Xiao, Huan Sun, Lei Li, Leon Derczynski, Anima Anandkumar, Fei Wang

Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity (2025.findings-emnlp)

Challenge:	a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs .
Approach:	This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs .
Outcome:	This tutorial aims to provide a systematic summary of risks and vulnerabilities in large language models . it will also outline emerging challenges in security, privacy and reliability of LLMs .

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Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang, Shuming Shi, Shiguo Lian