Papers by Heng Zhou
From Lists to Emojis: How Format Bias Affects Model Alignment (2025.acl-long)
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| Challenge: | Format biases in reinforcement learning from human feedback are underexplored . despite its effectiveness, RLHF faces challenges, including policy and regulatory constraints . |
| Approach: | They extend the study of preference biases beyond verbosity bias to a wider range of format biase . they show that with a small amount of biased data, they can inject significant bias into the reward model . |
| Outcome: | The proposed approach can be easily exploited by large language models to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena. |
KIA: Knowledge-Guided Implicit Vision-Language Alignment for Chest X-Ray Report Generation (2025.coling-main)
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| Challenge: | Existing reports on medical images and reports lack fine-grained cross-modal interaction, leading to insufficient understanding of detailed information. |
| Approach: | They propose a framework for establishing cross-modal semantic alignment in radiology report pairs using knowledge-guided implicit vision-language alignment. |
| Outcome: | KIA improves understanding of medical images and reports by incorporating medical knowledge to enhance pathological observation and anatomical landm. |
Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)
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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han
| Challenge: | Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs. |
| Approach: | They propose a system that extracts chemical reactions directly from raw scientific PDFs. |
| Outcome: | The proposed system can extract chemical reactions from raw scientific PDFs. |
MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications (2024.emnlp-demo)
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Xiangru Tang, Chunyuan Deng, Hanminwang Hanminwang, Haoran Wang, Yilun Zhao, Wenqi Shi, Yi Fung, Wangchunshu Zhou, Jiannan Cao, Heng Ji, Arman Cohan, Mark Gerstein
| Challenge: | Large language models (LLMs) have evolved into interactive agents capable of planning, tool use, and task execution across various tasks. |
| Approach: | They propose a platform that leverages large language models to generate agent-tuning data for fine-tuneing smaller, specialized models. |
| Outcome: | MIMIR enables large models to simulate various roles and create interaction data, which can then be used to fine-tune open-source models like LLaMA2. |
Human-in-the-loop Schema Induction (2023.acl-demo)
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Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Windisch Brown, Reece Suchocki, Chris Callison-Burch
| Challenge: | Existing approaches to event-centric natural language understanding (NLU) have been limited to linear and temporal ones. |
| Approach: | They propose a human-in-the-loop schema induction system powered by GPT-3 . they show that it transfers to new domains more easily than previous approaches . |
| Outcome: | The proposed system transfers to new domains more easily than previous approaches and reduces human curation. |
Video-Text Prompting for Weakly Supervised Spatio-Temporal Video Grounding (2024.emnlp-main)
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| Challenge: | Existing methods extract each candidate tube feature independently by cropping objects from video frame feature, discarding all contextual information such as position change and inter-entity relationship. |
| Approach: | They propose to use video-text prompts to construct candidate feature instead of cropping tube region from feature map . they also propose negative contrastive samples whose candidate object is erased instead of being highlighted . |
| Outcome: | The proposed methods surpass existing weakly-supervised methods by a great margin . they draw visual markers over objects tubes as video prompts . |
BadWindtunnel: Defending Backdoor in High-noise Simulated Training with Confidence Variance (2025.findings-acl)
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| Challenge: | Current backdoor attack defenders in NLP typically involve data reduction or model pruning, risking losing crucial information. |
| Approach: | They propose a backdoor defender that allows precise control over training conditions to model backdoor learning behavior without affecting the final model. |
| Outcome: | The proposed model reduces the backdoor learning behavior without affecting the final model. |
From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)
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Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, Bing Qin
| Challenge: | Existing Large Language Models (LLMs) struggle with implicit modality alignment and suboptimal graph linearization. |
| Approach: | They propose a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. |
| Outcome: | ExE-LLM outperforms fully trained graph neural networks on four benchmarks . it achieves SOTA performance in inductive settings, significantly outperforming fully trained neural networks . |
M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)
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Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo
| Challenge: | prevailing methods for machine translation are often hindered by misleading reward signals. |
| Approach: | They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors . |
| Outcome: | The proposed framework outperforms open-source models and achieves parity with proprietary models. |
FlashAudio: Rectified Flow for Fast and High-Fidelity Text-to-Audio Generation (2025.acl-long)
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| Challenge: | Recent advances in latent diffusion models (LDMs) have markedly enhanced text-to-audio generation, yet their iterative sampling processes impose substantial computational demands, limiting practical deployment. |
| Approach: | They propose to learn straight flow for fast simulation by using flashAudio with rectified flows and immiscible flow to minimize the total distance of data-noise pairs in a batch vias assignment. |
| Outcome: | The proposed method can learn straight flow for fast simulations and reduce noise distribution. |
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (2022.coling-1)
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| Challenge: | Document-level Event Factuality Identification (DEFI) is a fundamental and crucial task in NLP. |
| Approach: | They propose a framework for document-level event factuality identification (DEFI) they propose to use Span-Extraction and Multiple-Choice to model DEFI as machine reading comprehension tasks . |
| Outcome: | The proposed model outperforms state-of-the-art models on a document-based event factuality task . it uses Span-Extraction (Ext) and Multiple-Choice (Mch) knowledge to extract knowledge from large-scale MRC corpus . |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning (2026.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is a low-cost alternative to full fine-timing due to the massive overhead. |
| Approach: | They propose a Mixture-of-Experts approach that enhances specialization while maintaining low resource overhead. |
| Outcome: | The proposed approach outperforms or matches state-of-the-art methods on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Paper Abstract Writing through Editing Mechanism (P18-2)
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| Challenge: | a paper abstract writing system can automatically generate an abstract from a title . a typical recurrent neural network (RNN) based approach easily loses focus. |
| Approach: | They propose a paper abstract writing system that automatically generates an abstract from a title. |
| Outcome: | The proposed system passes Turing tests by junior domain experts and non-experts at a rate up to 80%. |
ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks (2025.emnlp-main)
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| Challenge: | Multi-agent systems (MAS) are limited by poor flexibility and scalability, with underdeveloped optimization strategies. |
| Approach: | They propose a task graph generation and a reward-driven two-stage agent selection process to integrate multi-agent systems to improve their reasoning capabilities. |
| Outcome: | The proposed model outperforms existing methods on Math-MAS and SciBench-MAS SciBech, while other methods completely fail. |
Text2DB: Integration-Aware Information Extraction with Large Language Model Agents (2024.findings-acl)
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| Challenge: | Current methods for information extraction (IE) focus on integrating IE output with the database . a long-overlooked question is what counts as "relevant knowledge" |
| Approach: | They propose a task that emphasizes integration of IE output and the database . they introduce a benchmark and an LLM agent framework for this task . |
| Outcome: | The proposed task integrates IE output and the target database (or knowledge base) it meets common demands such as data infilling, row population, and column addition . |
New Frontiers of Information Extraction (2022.naacl-tutorials)
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| Challenge: | Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data. |
| Approach: | This tutorial will provide an introduction to recent advances in IE by answering several important research questions. |
| Outcome: | The tutorial will address several important research questions and outline directions for further investigation. |
Event Causality Identification via Derivative Prompt Joint Learning (2022.coling-1)
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| Challenge: | Existing methods for event causality identification lack annotated data, and they lack the ability to identify explicit and implicit causality. |
| Approach: | They propose a derivative prompt joint learning model which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. |
| Outcome: | The proposed model can identify explicit and implicit causality on two benchmark datasets and it has great advantages over previous methods. |
Symbolization, Prompt, and Classification: A Framework for Implicit Speaker Identification in Novels (2023.findings-emnlp)
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| Challenge: | Existing methods for speaker identification in novel dialogues are limited to handling explicit narrative patterns and complex cases. |
| Approach: | They propose a framework which identifies implicit speakers in novels via symbolization, prompt, and classification. |
| Outcome: | The proposed framework outperforms existing methods by 4.8% accuracy on the web novel collection, which reduces 47% of speaker identification errors, and outperfies the emerging ChatGPT. |
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)
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Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou
| Challenge: | Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering. |
| Approach: | They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text. |
| Outcome: | The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings. |
Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition (D18-1)
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| Challenge: | a novel information network decipherment paradigm is proposed for fine-grained coordinated cross-lingual text stream alignment. |
| Approach: | They propose to use Burst Information Networks as media to represent text streams . they propose a simple yet effective information network decipherment algorithm with diverse clues . |
| Outcome: | The proposed approach outperforms existing approaches on bilingual lexicon extraction from coordinated text streams and can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining. |
Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation (2026.acl-long)
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Jiang Zhou, Xiaohu Zhao, Xinwei Wu, Tianyu Dong, Hao Wang, Yangyang Liu, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Deyi Xiong
| Challenge: | Current systems often fall short of this goal in settings where translation hinges on culturally grounded entities such as books, films, places, songs and idioms. |
| Approach: | They propose a framework that anchors supervision on a verifiable, entity-level reward signal and incorporates lightweight structural gates to stabilize optimization. |
| Outcome: | The proposed framework improves on XC-Translate and shows that it can learn a robust reasoning process rather than imitating reference translations. |
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment (2026.findings-acl)
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Tianyu Dong, Yangyang Liu, Jiang Zhou, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Shaolin Zhu, Deyi Xiong
| Challenge: | Low-resource language tokens are often routed to different experts than those activated by high-resourced inputs, which hinders their efficacy in multilingual contexts. |
| Approach: | They propose a framework to transfer specialized capabilities from high-resource languages as anchors to low-resourced languages by using a symmetric Jensen-Shannon constraint. |
| Outcome: | The proposed framework outperforms standard instruction tuning on 5 low-resource languages and 3 benchmarks. |
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)
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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai
| Challenge: | elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored. |
| Approach: | They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency . |
| Outcome: | The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training. |
Non-Sequential Graph Script Induction via Multimedia Grounding (2023.acl-long)
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| Challenge: | Existing scripts for everyday tasks are presented in a linear manner, which does not reflect the flexibility displayed by people executing tasks in real life. |
| Approach: | They propose to use loosely aligned videos to train a non-sequential graph script induction task by using a multimodal framework to ground procedural videos to WikiHow textual steps. |
| Outcome: | The proposed model outperforms the WikiHow linear baseline by 48.76% . it can predict future steps given a partial step sequence and generate explicit graph scripts . |
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning (2024.findings-acl)
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Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi Fung, Zhenhailong Wang, Lingyu Zhang, Shih-Fu Chang, Heng Ji
| Challenge: | LVLMs are known for producing text that is factually inconsistent with visual input . factuality of generated captions for structured visuals has not been studied as much . |
| Approach: | They propose a typology of factual errors in captions generated by large vision-language models . they propose CHOCOLATE, a visual entailment model that outperforms current models based on this analysis . |
| Outcome: | The proposed model outperforms current models in evaluating caption factuality. |
Enhanced Chart Understanding via Visual Language Pre-training on Plot Table Pairs (2023.findings-acl)
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| Challenge: | Existing methods to understand chart plots are difficult to apply to visual-language tasks. |
| Approach: | They propose a V+L model that learns how to interpret table information from chart images via cross-modal pre-training on plot table pairs. |
| Outcome: | The proposed model outperforms state-of-the-art models on the chartQA benchmark by over 8% performance gains. |