Papers by Jun Zhan
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (2023.acl-long)
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Jun Zhao, WenYu Zhan, Xin Zhao, Qi Zhang, Tao Gui, Zhongyu Wei, Junzhe Wang, Minlong Peng, Mingming Sun
| Challenge: | Existing methods for zero-shot relation extraction lack explicit modeling of matching pattern . et al. (2018) show that our method achieves higher matching accuracy and faster inference speed . |
| Approach: | They propose a fine-grained semantic matching method tailored for zero-shot relation extraction . they decompose sentence-level similarity score into entity matching score and context matching score . |
| Outcome: | The proposed method achieves higher matching accuracy and faster inference speed than state-of-the-art methods. |
Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards (2026.acl-long)
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| Challenge: | Large reasoning models are typically trained using reinforcement learning with verifiable reward (RLVR) positive and negative self-generated rollouts are used to update the model's policy . positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths. |
| Approach: | They propose a method that allocates advantage signals to key tokens across different polarities. |
| Outcome: | The proposed method improves the ability of large reasoning models to learn from their own generated rollouts. |
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)
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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu
| Challenge: | Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output. |
| Approach: | They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities. |
| Outcome: | The proposed model can be trained stably without any alterations to existing models or training paradigms. |
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (2022.emnlp-main)
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| Challenge: | Existing adversarial models rely on keyword matching and ignore relevant contextual relations for answer prediction. |
| Approach: | They propose to use keyword matching to attack model with two biases that rely on a perturbed answer sentence and a distracting answer sentence to misguide model. |
| Outcome: | The proposed method produces fluent and grammatical adversarial contexts while maintaining gold answers. |
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (2022.coling-1)
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| Challenge: | Existing methods to extract entities from visually-rich documents ignore the inherent multimodality of VRDs and thus the suboptimal results are achieved. |
| Approach: | They propose a multimodal semantic enhancement method that filters redundant information in the current document and a cross-document information awareness technique to enrich the entity-related context. |
| Outcome: | The proposed method outperforms existing methods on two documents understanding benchmarks covering eight languages. |
Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)
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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang
| Challenge: | Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals. |
| Approach: | They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals. |
| Outcome: | The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations. |
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities (2023.findings-emnlp)
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| Challenge: | Existing multi-modal large language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. |
| Approach: | They propose a large language model with intrinsic cross-modal conversational abilities . they construct a cross-text speech instruction dataset and employ a three-stage training strategy . |
| Outcome: | The proposed model can follow cross-modal human instructions and handle multiple modalities with one model. |
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)
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| Challenge: | Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs . |
| Approach: | They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique. |
| Outcome: | The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias. |
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs (2026.findings-acl)
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| Challenge: | Personalization can inadvertently distort factual reasoning when faced with factual queries. |
| Approach: | They propose a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. |
| Outcome: | Experiments across multiple LLM backbones and personalization methods show that FPPS significantly improves factual accuracy while maintaining personalized performance. |
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding (2026.acl-long)
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| Challenge: | Existing models for visual information extraction suffer from limitations in scale and realism . ReceiptBench is a large-scale, human-annotated benchmark for receipts . |
| Approach: | They propose a large-scale, human-annotated benchmark for visual information extraction . the method organizes information extraction into four hierarchical sub-tasks . |
| Outcome: | The proposed method surpasses proprietary models on complex reasoning tasks. |