Papers by Huan Liu

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

Challenge:	Existing research lacks direct access to such data, making benchmarking difficult due to privacy concerns.
Approach:	They propose a synthetic data pipeline that generates realistic user profiles and private documents and a benchmark to evaluate models' ability to understand personal information.
Outcome:	The proposed pipeline generates realistic user profiles and private documents, enabling PersonaBench, a benchmark for evaluating models’ ability to understand personal information.

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

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

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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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.

How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts (2023.findings-emnlp)

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Tharindu Kumarage, Paras Sheth, Raha Moraffah, Joshua Garland, Huan Liu

ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning (2026.acl-long)

Challenge:	Existing methods to detect AI-generated text are inadequate, causing misuse of the text.
Approach:	They propose a universal evasive prompt framework that can prompt any PLM to generate “human-like” text that can mislead detectors.
Outcome:	The proposed approach can prompt any PLM to generate “human-like” text that can mislead detectors.

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Xuan Xiong, Huan Liu, Li Gu, Zhixiang Chi, Yue Qiu, Yuanhao YU, Yang Wang

Learning to Selectively Learn for Weakly-supervised Paraphrase Generation (2021.emnlp-main)

Challenge:	Existing methods to shorten CoTs use length penalties or global entropy reduction . Existing approaches to CoT reasoning have significant practical drawbacks .
Approach:	They propose a method that shortens CoTs by length penalties or global entropy reduction . they integrate ETR into Group Relative Policy Optimization and evaluate it .
Outcome:	The proposed objective improves accuracy–efficiency trade-off by +9.9% while reducing CoT length by 67% across four benchmarks.

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Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu

Text Generation

Challenge:	Existing approaches to generate paraphrases with weak supervision are limited in real-world scenarios due to the lack of coherent and controllable generated paraphrase.
Approach:	They propose a method to generate high-quality paraphrases with weak supervision . they obtain abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion .
Outcome:	The proposed approach achieves significant improvements over existing methods and is even comparable in performance with supervised state-of-the-arts.

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

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

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

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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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.

Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference (D19-1)

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Ahmadreza Mosallanezhad, Ghazaleh Beigi, Huan Liu

Defending Against Social Engineering Attacks in the Age of LLMs (2024.emnlp-main)

Challenge:	Recent research shows textual data alone may contain enough information about users' private-attributes that they do not want to disclose such as age, gender, location, political views and sexual orientation.
Approach:	They propose a novel Reinforcement Learning-based Text Anonymizor which extracts a latent representation of the original text w.r.t. a given task and leverages deep reinforcement learning to learn an optimal strategy for manipulating text representations w/ the received privacy and utility feedback.
Outcome:	The proposed approach preserves both privacy and utility of textual data while preserving its utility.

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Lin Ai, Tharindu Kumarage, Amrita Bhattacharjee, Zizhou Liu, Zheng Hui, Michael Davinroy, James Cook, Laura Cassani, Kirill Trapeznikov, Matthias Kirchner, Arslan Basharat, Anthony Hoogs, Joshua Garland, Huan Liu, Julia Hirschberg

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

Challenge:	Existing research has developed frameworks to understand human-to-human CSE attacks.
Approach:	They propose a modular defense pipeline that improves detection at both the message and conversation levels.
Outcome:	The proposed model can be exploited to facilitate chat-based social engineering attacks and generate high-quality CSE content, but their detection capabilities are suboptimal, leading to increased operational costs for defense.

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

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)

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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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong

ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents (2026.findings-acl)

Challenge:	DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say .
Approach:	They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset .
Outcome:	a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data .

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Dawei Li, Yuguang Yao, Zhen Tan, Huan Liu, Ruocheng Guo

Challenge:	Reward-guided search methods have shown potential in enhancing tool-using agents . however, there is a lack of reliable evaluation benchmarks for PRMs in tool-use settings .
Approach:	They propose a large-scale benchmark specifically designed to evaluate PRMs for tool-using agents.
Outcome:	The proposed benchmark shows that tool reward models perform better in tool-using environments.

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification (2020.emnlp-main)

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Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu

Information Extraction & Text Mining

Challenge:	Text classification is a critical research topic with broad applications in natural language processing. graph neural networks (GNNs) have received increasing attention but their performance is jeopardized in practice.
Approach:	They propose a model which captures long-distance interactions between words and a graph-based model which can be used to perform text classification.
Outcome:	The proposed model can achieve more expressive power with less computational consumption on the text classification task.

Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models (2025.acl-long)

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Guanghui Ye, Huan Zhao, Zhixue Zhao, Xupeng Zha, Yang Liu, Zhihua Jiang

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens (2026.findings-acl)

Challenge:	Knowledge-based visual reasoning (KB-VR) is a challenging task, as it requires machines not only to understand concepts and relationships of visual scenes, but also to associate them with external world knowledge to perform chain of reasoning on open-world questions.
Approach:	They propose a visual knowledge card (VKC) that integrates internal visual knowledge and external world knowledge produced by a knowledge generator into an image.
Outcome:	The proposed model achieves new state-of-the-art results compared to previous top-performing models on three popular KB-VR benchmarks.

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Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu

Reasoning

Challenge:	Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning from large language models (LLMs).
Approach:	They propose a data distribution lens to understand when and why CoT reasoning fails . they propose 'data-based' training that trains LLMs from scratch .
Outcome:	The proposed model enables models to generate reasoning trajectories that approximate those observed during training.

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

Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms (2023.acl-long)

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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Tianshu Zhang, Changchang Liu, Wei-Han Lee, Yu Su, Huan Sun

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

Challenge:	Neural semantic parsers have achieved remarkable performance in recent years, but they are data-hungry and require annotators to have intimate knowledge of formal programs.
Approach:	They propose a task where multiple clients collaboratively train one global model without sharing their semantic parsing data.
Outcome:	The proposed model improves performance on three widely adopted FL algorithms (FedAvg, FedOPT and FedProx) and clients with smaller datasets enjoy faster performance.

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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

Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)

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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Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang

Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism (2026.acl-long)

Challenge:	Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses.
Approach:	They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses.
Outcome:	The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses.

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Yuhao Shen, Tianyu Liu, Junyi Shen, Jinyang Wu, Quan Kong, Huan Li, Cong Wang

Information Retrieval

Challenge:	Parallel Speculative Decoding (PSD) has limitations due to speedup limits and high computational waste . a novel synchronous mechanism solves the Retrieval Precision-Efficiency Dilemma .
Approach:	They propose a framework that combines a draft-verification-based approach with a synchronous mechanism to solve the Retrieval Precision-Efficiency Dilemma.
Outcome:	The proposed framework breaks speedup limits for Speculative Decoding by overlapping draft generation with verification.

AGTAO: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality (2026.findings-acl)

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Pengyu Li, Lingling Zhang, Zhitao Gao, Yanrui Wu, Yuxuan Dong, Huan Liu, Bifan Wei, Jun Liu

Adaptive Token-level Cross-lingual Feature Mixing for Multilingual Neural Machine Translation (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) unintentionally memorize sensitive data, posing privacy and security risks.
Approach:	They propose a framework that reconciles unlearning efficacy and utility preservation by using a latent-space gating mechanism to simulate internal recovery attempts.
Outcome:	The proposed framework achieves superior trade-off between unlearning efficacy and model utility.

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Junpeng Liu, Kaiyu Huang, Jiuyi Li, Huan Liu, Jinsong Su, Degen Huang

Multilinguality

Challenge:	Multilingual neural machine translation models can translate multiple language pairs in a single model but lacks ability to capture language-specific features.
Approach:	They propose a token-level feature mixing method that captures different features and dynamically determines feature sharing across languages.
Outcome:	The proposed method outperforms baselines and can be extended to zero-shot translation.

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

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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

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

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

LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback (2025.findings-acl)

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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Thai Quoc Hoang, Kung-Hsiang Huang, Shirley Kokane, Jianguo Zhang, Zuxin Liu, Ming Zhu, Jake Grigsby, Tian Lan, Michael S Ryoo, Chien-Sheng Wu, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles

Challenge:	Large Action Models (LAMs) face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback.
Approach:	They propose a framework for online exploration of agentic tasks with high-quality feedback . they use a dynamic task query generator and an extensive collection of tools to create a high-level feedback environment for LLM Agents.
Outcome:	The proposed framework achieves 49.3% performance improvement over baselines on toolbench and CRMArena.

Debiasing Word Embeddings with Nonlinear Geometry (2022.coling-1)

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Lu Cheng, Nayoung Kim, Huan Liu

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)

Challenge:	Existing methods for debiasing word embeddings are limited to individual social categories . however, real-world corpora typically present multiple social categories that may correlate or intersect with each other.
Approach:	They propose a method to debias word embeddings using nonlinear geometry of individual biases.
Outcome:	Empirical results show that the proposed method mitigates biases associated with individual social categories and treats each category in isolation.

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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Structured Data in NLP

Challenge:	Existing methods for QA in industrial environments are inherently relational and often updated.
Approach:	They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning.
Outcome:	Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity.

World Models with Hints of Large Language Models for Goal Achieving (2025.naacl-long)

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Zeyuan Liu, Ziyu Huan, Xiyao Wang, Jiafei Lyu, Jian Tao, Xiu Li, Furong Huang, Huazhe Xu

LATTE: Learning to Think with Vision Specialists (2025.emnlp-main)

Challenge:	Existing methods address this by adding intrinsic rewards, but they fail to provide meaningful guidance in long-horizon decision-making tasks with large state and action spaces lacking purposeful exploration.
Approach:	They propose a multi-modal model-based RL approach that integrates the proposed hinting subgoals into the model rollouts to encourage goal discovery and reaching in challenging tasks.
Outcome:	The proposed model outperforms existing methods in challenging, sparse-reward environments such as HomeGrid, Crafter, and Minecraft by 41.8%, 21.1%, and 9.9%.

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Zixian Ma, Jianguo Zhang, Zhiwei Liu, Jieyu Zhang, Juntao Tan, Manli Shu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Caiming Xiong, Ranjay Krishna, Silvio Savarese

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation (2023.acl-long)

Challenge:	Open-source vision-language models excel on simple question-answering tasks, but struggle with complex questions that require both perception and reasoning.
Approach:	They propose a family of vision-language models that have LeArned to Think wiTh vision spEcialists by offloading perception to state-of-the-art vision models.
Outcome:	The proposed model achieves 4-5% gains over baselines across 6 benchmarks covering both perception and reasoning abilities.

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Zhiwei Cao, Baosong Yang, Huan Lin, Suhang Wu, Xiangpeng Wei, Dayiheng Liu, Jun Xie, Min Zhang, Jinsong Su

Machine Translation

Challenge:	Existing methods to improve k-Nearest neighbor machine translation (kNN-MT) are based on the ability to non-parametrically adapt to new domains.
Approach:	They propose a method to boost the datastore retrieval of k-Nearest neighbor machine translation by reconstructing the original datastore.
Outcome:	The proposed method boosts the retrieval and translation quality of k-Nearest neighbor machine translation by reconstructing the original datastore.

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

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

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.

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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.

Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)

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Huan Lin, Baosong Yang, Liang Yao, Dayiheng Liu, Haibo Zhang, Jun Xie, Min Zhang, Jinsong Su

Multilinguality

Challenge:	Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity .
Approach:	They propose a multi-candidate optimization framework for diverse NMT to deal with this defect.
Outcome:	The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality.

JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning (2024.acl-demos)

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Anique Tahir, Lu Cheng, Huan Liu

Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026.acl-industry)

Challenge:	Large Language Models (LLMs) face significant memory constraints when fine-tuning large prompt sequences.
Approach:	They propose a framework for PEFT-compatible fine-tuning of large language models, leveraging distributed training.
Outcome:	The proposed framework improves performance 12x compared to Hugging Face/DeepSpeed implementation with four GPUs while consuming less than half the VRAM per GPU.

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Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

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

Challenge:	Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet traditional singleround retrieval struggles with complex multistep reasoning.
Approach:	They propose a framework that introduces path-centric reward shaping for agentic RAG training.
Outcome:	The proposed framework improves on existing methods with an average accuracy gain of 7.7 points.

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Wenxuan Zhou, Fangyu Liu, Huan Zhang, Muhao Chen

Global Relation Embedding for Relation Extraction (N18-1)

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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Yu Su, Honglei Liu, Semih Yavuz, Izzeddin Gür, Huan Sun, Xifeng Yan

Information Extraction & Text Mining

Challenge:	Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data.
Approach:	They propose to embed relations with global statistics of relations to combat the wrong labeling problem of distant supervision.
Outcome:	The proposed method is more robust to training noise introduced by distant supervision and improves relation extraction models.

Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach (2021.acl-long)

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Lu Cheng, Ahmadreza Mosallanezhad, Yasin Silva, Deborah Hall, Huan Liu

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)

Challenge:	Existing efforts to enhance the performance of session-based cyberbullying detection have overlooked unintended social biases in existing datasets.
Approach:	They propose a model-agnostic debiasing strategy that leverages a reinforcement learning technique to mitigate unintended biases in existing datasets.
Outcome:	The proposed approach can mitigate unintended biases without impairing the detection performance.

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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao

Reasoning

Challenge:	Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems.
Approach:	They propose a hierarchical benchmark to evaluate large language models on engineering problems.
Outcome:	The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields.

Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges (2025.findings-acl)

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Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong

Exploiting Class Probabilities for Black-box Sentence-level Attacks (2024.findings-eacl)

Challenge:	Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use focus on stateless, single-turn interactions or partial evaluations, overlooking the inherent stateful nature of interactions in multi-turn applications.
Approach:	They propose a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use across six key tasks in three stages . they also build VirtualMobile – an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs.
Outcome:	The proposed dataset evaluates 13 open- and closed-source LLMs and provides detailed analysis at each stage.

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Raha Moraffah, Huan Liu

Information Extraction & Text Mining

Challenge:	Existing sentences use either no feedback or only the class labels for sentence-level attacks.
Approach:	They propose an algorithm that uses class probabilities for black-box sentence-level attacks and investigate the effectiveness of using class probabilties on the attack’s success.
Outcome:	The proposed algorithm is evaluated against baselines and classifiers and compares with the existing models to determine whether it is worthy or practical to use class probabilities for black-box sentence-level attacks.

NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)

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Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan

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

Challenge:	Existing work generates long videos segment by segment sequentially, which is inefficient.
Approach:	They propose a Diffusion over Difference architecture for eXtremely Long video generation.
Outcome:	The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence.

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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

HoneyComb: A Flexible LLM-Based Agent System for Materials Science (2024.findings-emnlp)

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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Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, Bang Liu

Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)

Challenge:	specialized large language models (LLMs) have shown promise in materials science but often struggle with the distinct complexities of materials science tasks.
Approach:	They propose a new LLM-based agent system specifically designed for materials science that leverages a reliable materials science knowledge base and a sophisticated tool hub.
Outcome:	The proposed system outperforms baseline models across tasks in materials science while ensuring accuracy and relevance.

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Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu

Seeing Beyond Words: MatVQA for Challenging Visual-Scientific Reasoning in Materials Science (2026.findings-acl)

Challenge:	Existing surveys focus on LLMs' specific utility for data annotation and synthesis.
Approach:	They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations .
Outcome:	The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information.

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Sifan Wu, Huan Zhang, Yizhan Li, Farshid Effaty, Hongyuan Mei, Amirreza Ataei, Bang Liu

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

Challenge:	Multimodal Large Language Models (MLLMs) outperform existing benchmarks in both natural language and coding domains.
Approach:	They propose a scalable benchmark that integrates vision and language modalities to address this gap by eliminating textual shortcuts.
Outcome:	The new benchmark outperforms existing benchmarks in both natural language and coding domains.

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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

LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation (2026.findings-eacl)

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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Yupian Lin, Guangya Yu, Cheng Yuan, Huan Du, Hui Luo, Yuang Bian, Jingping Liu, Zhidong He, Wen Du, Tong Ruan

Towards User-Driven Neural Machine Translation (2021.acl-long)

Challenge:	Existing LLMs are difficult to achieve satisfactory results in table-related tasks.
Approach:	They propose to develop a specialized logical table-to-text generation model that can be used for table-related tasks.
Outcome:	The proposed model achieves state-of-the-art on a Logic2Text dataset.

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Huan Lin, Liang Yao, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Degen Huang, Jinsong Su

Text Generation

Challenge:	a good translation should implicitly mirror user traits rather than translate the original content semantically.
Approach:	They propose a framework that captures user traits from historical inputs . they propose 'user-driven' NMT to model user behavior under a zero-shot learning fashion .
Outcome:	The proposed framework can capture user traits from historical inputs under zero-shot learning fashion.

SlackAgents: Scalable Collaboration of AI Agents in Workspaces (2025.emnlp-demos)

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Zhiwei Liu, Weiran Yao, Zuxin Liu, Juntao Tan, Jianguo Zhang, Frank Wang, Sukhandeep Nahal, Huan Wang, Shelby Heinecke, Silvio Savarese, Caiming Xiong

HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science (2023.findings-emnlp)

Challenge:	Existing open-source frameworks like LangChain and LlamaIndex fail to integrate into daily workflows, resulting in limited daily usage for work.
Approach:	They propose a multi-agent library for scalable management and collaboration of AI agents on Slack.
Outcome:	The proposed framework offers instant AI integration into organizational workflows and facilitates scalable collaboration, allowing for effective communication and task orchestration.

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Yu Song, Santiago Miret, Huan Zhang, Bang Liu

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

Challenge:	LLaMa-based language model for materials science is first of its kind in the world .
Approach:	They propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct) they then apply this process to finetune a LLaMa-based language model targeted for materials science.
Outcome:	The proposed model outperforms existing language models on materials science tasks and improves in successive stages of refinement.

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