Papers by Yuhan Liu
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)
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| Challenge: | a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations. |
| Approach: | They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting . |
| Outcome: | The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations. |
EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly used for automated negotiation, but their cloud-centric paradigm exposes sensitive negotiations to privacy and security risks. |
| Approach: | They propose a Bayesian multi-agent framework that transforms emotional decision-making from reactive to strategic. |
| Outcome: | EmoMAS leverages a Bayesian orchestrator to coordinate three specialized agents: game-theoretic, reinforcement learning, and psychological coherence models. |
AS-ES Learning: Towards efficient CoT learning in small models (2024.findings-acl)
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| Challenge: | Existing methods to induce Chain-of-Thought (CoT) in LLMs are limited and do not consider the importance of efficiently utilizing existing CoT data. |
| Approach: | They propose a new training paradigm which exploits the inherent information in CoT for iterative generation. |
| Outcome: | The proposed training paradigm surpasses direct seq2seq training on CoT-extensive tasks without data augmentation or altering the model itself. |
The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents (2025.emnlp-main)
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| Challenge: | Existing studies assume fake news is inherently existing rather than exploring its gradual formation. |
| Approach: | They propose a Large Language Model-based simulation approach explicitly focusing on fake news evolution from real news. |
| Outcome: | The proposed framework captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations. |
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)
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Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu
| Challenge: | Existing frameworks for Augmented Language Models lack flexibility, democratization, and holistic evaluation. |
| Approach: | They propose a lightweight and extensible framework for Augmented Language Models called Gentopia. |
| Outcome: | The proposed framework integrates language models, task formats, prompting modules, and plugins into a unified paradigm. |
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)
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| Challenge: | Existing platforms lack a mechanism for user actions to dynamically reshape the environment. |
| Approach: | They propose a novel agent-based simulation platform for recommender systems with a robust interaction mechanism. |
| Outcome: | The proposed platform improves the credibility of the simulation and replicates the Matthew Effect and Brand Loyalty. |
Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
| Approach: | They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge. |
| Outcome: | The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. |
Forest Before Trees: Latent Superposition for Efficient Visual Reasoning (2026.acl-long)
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| Challenge: | Recent latent reasoning methods suffer from a bandwidth bottleneck . explicit textual rationales suffer from premature semantic collapse . |
| Approach: | They propose a new paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance among latent reasoning methods surpassing the strong baseline Monet by 5.03% on average. |
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)
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Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma
| Challenge: | Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation. |
| Approach: | They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset. |
| Outcome: | The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations. |
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)
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| Challenge: | Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy . |
| Approach: | They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels. |
| Outcome: | The proposed framework improves empathetic response generation by incorporating emotion cause information into the model. |
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation systems face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. |
| Approach: | They propose an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. |
| Outcome: | The proposed framework achieves dual improvements in retrieval precision and generation quality without additional training or API resources while using only 40% of the tokens compared to traditional approaches. |
User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal (2025.emnlp-main)
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| Challenge: | a recent study shows that asking for direct user feedback can be disruptive . we examine whether incorporating the contents of user feedback improves model performance . |
| Approach: | They analyze user feedback in the user-LLM conversation logs and harvest learning signals from it. |
| Outcome: | The proposed approach can lead to model degradation on two user-LM interaction datasets. |
DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback (2025.findings-emnlp)
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| Challenge: | Multi-agent systems (MAS) powered by large language models struggle to adapt to evolving task dependencies and to handle uncertainties. |
| Approach: | They propose a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning. |
| Outcome: | The proposed framework outperforms traditional reinforcement learning and human-agent collaboration in the Overcooked simulation. |
Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration (2024.emnlp-main)
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Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, Yulia Tsvetkov
| Challenge: | Existing alignment paradigms for large language models learn an averaged human preference and struggle to model diverse preferences across cultures, demographics, and communities. |
| Approach: | They propose a modular framework that "plugs" into a base LLM a pool of smaller but specialized community LMs where models collaborate in distinct modes to support three modes of pluralism: Overton, steerable, and distributional. |
| Outcome: | The proposed framework “plugs into” a base LLM a pool of smaller but specialized community LMs, where models collaborate in distinct modes to support three modes of pluralism: Overton, steerable, and distributional. |
Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models (2026.findings-acl)
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| Challenge: | Large audio-language models (LALMs) can exhibit a temporal smoothing bias . unified decoders can produce less specific audio-grounded outputs . |
| Approach: | They propose a temporally blurred slow-path view that is re-encoded by a token-level logit update. |
| Outcome: | Experiments on MMAU and AIR-Bench show consistent improvements on strong unified LALMs. |
Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)
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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao
| Challenge: | Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception. |
| Approach: | They propose a framework that organizes audio information into three explicit components in a unified JSON format. |
| Outcome: | The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities. |
P3Sum: Preserving Author’s Perspective in News Summarization with Diffusion Language Models (2024.naacl-long)
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Yuhan Liu, Shangbin Feng, Xiaochuang Han, Vidhisha Balachandran, Chan Young Park, Sachin Kumar, Yulia Tsvetkov
| Challenge: | Existing summarization systems alter the political opinions and stances of news articles in more than 50% of summaries, misrepresenting the intent and perspectives of the authors. |
| Approach: | They propose a model-based summarization approach controlled by political perspective classifiers that preserves the political stance of a generated summary. |
| Outcome: | The proposed model outperforms state-of-the-art summarization systems and large language models by up to 13.7% in terms of success rate of stance preservation, with competitive performance on standard metrics of summarizing quality. |
STEP: Success-Rate-Aware Trajectory-Efficient Policy Optimization (2026.findings-acl)
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| Challenge: | Existing GRPO-based methods allocate sampling uniformly across tasks regardless of difficulty, propagate misleading learning signals and incur high sample-collection costs. |
| Approach: | They propose a framework that allocates sampling based on per-task success rates and performs fine-grained step-level optimization. |
| Outcome: | The proposed method improves sample efficiency and training stability over existing GRPO variants and three ablation variants on OSWorld and AndroidWorld. |
Detecting AI-Generated Video: A Vision–Language Dual-View Survey (2026.findings-acl)
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| Challenge: | realism of AI-generated Videos (AIGC-V) rendering artifact-centric detection insufficient, authors argue . a vision–language dual-view taxonomy is proposed to systematize this rapidly evolving field . |
| Approach: | They propose a Vision–Language Dual-View taxonomy to systematize AIGC-V detection . they propose realism of AI-generated Videos is rendering traditional inspection insufficient . |
| Outcome: | The proposed model aims to show that the existing methods are consistent with real-world facts. |
Quest2DataAgent: Automating End-to-End Scientific Data Collection (2025.emnlp-demos)
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Tianyu Yang, Yuhan Liu, Sobin Alosious, Ethan A. Brown, Jason R. Rohr, Tengfei Luo, Xiangliang Zhang
| Challenge: | Existing approaches for data collection are labor-intensive and dependent on domain expertise. |
| Approach: | They propose a general-purpose multi-agent framework for automating scientific data collection workflows. |
| Outcome: | The proposed framework improves data relevance, usability, and time efficiency over existing methods. |
Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains (2025.acl-long)
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| Challenge: | Existing vision-language models struggle to disentangle information scattered across complex visual inputs, leading to performance degradation. |
| Approach: | They propose a focus-centric visual chain paradigm that enhances VLMs’ perception, comprehension, and reasoning abilities in multi-image scenarios. |
| Outcome: | The proposed approach achieves average performance gains of 3.16% and 2.24% across two distinct model architectures, without compromising the general vision-language capabilities. |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
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| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement (2025.findings-acl)
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| Challenge: | Recent research indicates that large language models (LLMs) have demonstrated remark-able capabilities in various programming-related domains, such as code generation and code refinement. |
| Approach: | They propose a framework that combines exploration with refinement to reduce test-time computation overhead. |
| Outcome: | The proposed framework outperforms SOTA and AgentCoder on humanEval and MBPP benchmarks while reducing test-time computation overhead and scalability. |
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety (2025.emnlp-main)
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Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang
| 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. |
H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)
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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li
| Challenge: | Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime . |
| Approach: | They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM . |
| Outcome: | Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets . |
HoPE: A Novel Positional Encoding Without Long-Term Decay for Enhanced Context Awareness and Extrapolation (2025.acl-long)
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| Challenge: | Existing positional encodings exhibit long-term decay, based on an entrenched and long-standing opinion that tokens farther away from the current position carry less relevant information. |
| Approach: | They propose a high-frequency rotary position encoding (HoPE) that replaces specific components in RoPE with position-independent ones, retaining only high- frequency signals. |
| Outcome: | The proposed method exhibits greater robustness to the out-of-distribution behavior in attention patterns during extrapolation. |
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)
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Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng
| Challenge: | Large Language Models (LLMs) have paved the way for complex tasks such as role-playing. |
| Approach: | They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models. |
| Outcome: | The proposed framework improves role-playing abilities with 168,093 samples. |
From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation (2026.findings-acl)
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| Challenge: | Novels create rich, immersive worlds with intricate plots and distinct styles, captivating readers through complex storytelling. |
| Approach: | They propose a novel generation system that imitates novel elements by predicting plot developments and writing concrete details using vivid, expressive language. |
| Outcome: | The novel imitative novel generation system is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence. |
IAD: In-Context Learning Ability Decoupler of Large Language Models in Meta-Training (2024.lrec-main)
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| Challenge: | In-context Learning (ICL) is a paradigm in which LLMs acquire task-specific knowledge by processing input-output pairs provided as prompts. |
| Approach: | They propose an In-context learning Ability Decoupler to separate ICL ability from general ability of LLMs in meta-training phase . they first identify parameters suitable for ICL by transference-driven gradient importance and propose a new max-margin loss to emphasize the separation of the two abilities. |
| Outcome: | The proposed model separates the ICL ability from the general ability of LLMs in the meta-training phase, where the I-related parameters are tuned to adapt for ICL tasks. |
DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis (2023.findings-acl)
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Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji
| Challenge: | a new task of conversational aspect-based sentiment analysis (DiaASQ) is designed to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. |
| Approach: | They propose a task of conversational aspect-based sentiment quadruple analysis to detect the quadrangle of target-aspect-opinion-sentiment in a dialogue. |
| Outcome: | The proposed task is based on a high-quality dataset in Chinese and English . it improves the end-to-end quadruple prediction and integrates rich feature representations . |
More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives (2025.acl-long)
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Xiaoqing Zhang, Ang Lv, Yuhan Liu, Flood Sung, Wei Liu, Jian Luan, Shuo Shang, Xiuying Chen, Rui Yan
| Challenge: | Large language models excel at few-shot in-context learning but performance plateaus as ICL demonstrations increase from a few to many. |
| Approach: | They propose a novel optimization method that optimizes the negative log-likelihood objective and reweights the model to achieve many-shot performance. |
| Outcome: | The proposed method achieves significant performance improvements across a large-scale dataset. |
Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)
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| Challenge: | Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory. |
| Approach: | They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text. |
| Outcome: | The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text. |
GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)
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| Challenge: | Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary. |
| Approach: | They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge. |
| Outcome: | The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations. |
Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models (2024.findings-acl)
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Wenxuan Ding, Shangbin Feng, Yuhan Liu, Zhaoxuan Tan, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov
| Challenge: | Existing tasks and datasets assess LLM knowledge abilities mostly by focusing on atomic (e.g., open-domain QA) or linear (e-hop QA). |
| Approach: | They propose a geometric knowledge reasoning benchmark consisting of incomplete knowledge networks bounded by structured factual constraints where LLMs are tasked with inferring the missing facts to meet all constraints. |
| Outcome: | The proposed methods outperform baseline methods and are more robust towards problems in the hard subset. |