Papers by Huan Zhao
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (2022.findings-emnlp)
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| Challenge: | Existing data augmentation methods for event extraction are costly and time-consuming. |
| Approach: | They propose a data augmentation framework that randomly masks out an adjunct sentence fragment and infills a variable-length text span with a fine-tuned infilling model. |
| Outcome: | The proposed framework can generate more diverse data while keeping the original structure unchanged . it can replace a fragment of arbitrary length in the text with another fragment of variable length . |
MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection (2026.acl-long)
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| Challenge: | Existing methods for multimodal stance detection face contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. |
| Approach: | They propose a multi-agent framework that integrates Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, Reasoning-Enhanced Debate stage and Self-Reflection for robust adjudication. |
| Outcome: | Extensive experiments on five datasets show that the proposed framework outperforms state-of-the-art methods. |
Automating Android Build Repair: Bridging the Reasoning-Execution Gap in LLM Agents with Domain-Specific Tools (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) have been shown to be useful for building applications, but their use for fixing Android build errors remains underexplored. |
| Approach: | They propose a large-level language model agent with domain-specific tools for inspecting and manipulating the Gradle build environment. |
| Outcome: | The proposed agent outperforms a state-of-the-art coding agent that relies on a general-purpose shell significantly on 184 build errors. |
ReportLogic: Evaluating Logical Quality in Deep Research Reports (2026.acl-long)
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| Challenge: | Existing evaluation frameworks that evaluate large language models for Deep Research largely ignore this requirement. |
| Approach: | They propose a benchmark that quantifies report-level logical quality through a reader-centric lens of auditability. |
| Outcome: | The proposed model quantifies logical quality through a reader-centric lens of auditability. |
Refusal-Aware Red Teaming: Exposing Inconsistency in Safety Evaluations (2025.emnlp-main)
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| 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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| 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. |
LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback (2024.naacl-long)
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| Challenge: | Existing dialogue systems do not utilize quality dimensions specifically designed for dialogue evaluation to guide the response generation during training. |
| Approach: | They propose a two-stage framework which generates and utilizes conversation evaluation as explicit feedback during training. |
| Outcome: | The proposed framework generates and utilizes conversation evaluation as explicit feedback during training. |
LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have a high vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Approach: | They propose to use large language models to test their security against jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Outcome: | The proposed model is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories. |
Automatic and Reliable Evaluation for Academic Caption-to-Figure Generation with LMMs (2026.acl-long)
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| Challenge: | Existing datasets for evaluating text-to-image generation focus mostly on real-life images, which poses challenges for assessing academic figure generation given real scientific captions. |
| Approach: | They propose a dataset that first provides a Holistic Evaluation for Academic caption-to-Figure Generation (HE4AFG) they collect real figure captions from 8 scientific domains and generate 3,900 evaluation samples . |
| Outcome: | The proposed model provides high-quality human ratings in terms of three aspects—scientific aesthetic (SA), topic relevance (TR), and attribute correctness (AC). |
Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models (2025.acl-long)
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| 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. |
EmoTransKG: An Innovative Emotion Knowledge Graph to Reveal Emotion Transformation (2024.findings-acl)
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| Challenge: | EmoTransKG establishes connections and transformations between emotions across open-textual events. |
| Approach: | They propose an Emotion Knowledge Graph that establishes connections and transformations between emotions across diverse open-textual events. |
| Outcome: | The proposed model integrates with existing conversational emotion recognition models to improve the quality and effectiveness of EmoTransKG. |
Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens (2026.findings-acl)
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Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu
| 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. |
Adversarial Training for Code Retrieval with Question-Description Relevance Regularization (2020.findings-emnlp)
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| Challenge: | Existing methods for code retrieval are based on question-description relevance . code retrievals are a key task aiming to match natural and programming languages . |
| Approach: | They propose to use question-description relevance to regularize adversarial learning for code retrieval . they adapt a simple adversarial learning technique to generate difficult code snippets . |
| Outcome: | The proposed method can improve the performance of state-of-the-art models on large-scale code retrieval datasets of two programming languages. |
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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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. |
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
| 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 . |
Glue pizza and eat rocks - Exploiting Vulnerabilities in Retrieval-Augmented Generative Models (2024.emnlp-main)
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| 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. |
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation (2021.naacl-main)
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| Challenge: | Recent studies show that NLP models are vulnerable to adversarial perturbations such as synonym substitutions or syntax-guided paraphrasing. |
| Approach: | They propose a “double perturbation” framework to uncover model weaknesses beyond the test dataset. |
| Outcome: | The proposed attack achieves high success rates on both original and robustly trained CNNs and Transformers. |
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
| 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. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
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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
| 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. |
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering (2022.acl-long)
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| Challenge: | Existing work exploits easily accessible co-occurrence information of events to learn event representations. |
| Approach: | They propose a weakly supervised contrastive learning method and a prototype-based clustering method for event representation learning. |
| Outcome: | The proposed framework outperforms baselines on Hard Similarity and Transitive Sentence Similarity tasks. |
Evo-PI: Aligning Medical Reasoning via Evolving Principle-Guided Supervision (2026.acl-long)
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| Challenge: | Existing models with static prompts, rules, or reward models are constrained by static supervision, which often fails to shape the underlying reasoning process, leading to brittle generalization and performance saturation in complex decision-making tasks. |
| Approach: | They propose a principle-centric learning framework that treats reasoning principles as explicit, language-based supervision signals that can be generated, evaluated, and iteratively evolved. |
| Outcome: | The proposed framework treats reasoning principles as explicit, language-based supervision signals that can be generated, evaluated, and iteratively evolved. |