Papers by Zengfeng Huang
HAF-RM: A Hybrid Alignment Framework for Reward Model Training (2025.acl-long)
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Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards. |
| Approach: | They propose a hybrid alignment framework **HAF-RM** that incorporates additional constraint on token-level policy probabilities in addition to the reward score. |
| Outcome: | The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level. |
PathQG: Neural Question Generation from Facts (2020.emnlp-main)
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| Challenge: | Existing research for question generation encodes text as a sequence of tokens without explicitly modeling fact information. |
| Approach: | They propose to incorporate facts in the input text for question generation in a comprehensive way. |
| Outcome: | The proposed model outperforms state-of-the-art models and human evaluation shows it generates relevant and informative questions. |
Retrieval-Augmented Language Models are Mimetic Theorem Provers (2025.findings-emnlp)
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Wenjie Yang, Ruiyuan Huang, Jiaxing Guo, Zicheng Lyu, Tongshan Xu, Shengzhong Zhang, Lun Du, Da Zheng, Zengfeng Huang
| Challenge: | Large language models often fail to provide rigorous proof-based reasoning for research-level mathematics. |
| Approach: | They propose a simple yet effective RAG framework that augments retrieved proofs with queries and document contexts to improve retrieval performance. |
| Outcome: | The proposed framework improves retrieval performance by 34.19% . dual RAG can be used to prove research-level theorems in theoretical machine learning . |
Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020.findings-emnlp)
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Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang
| Challenge: | Gene Ontology (GO) terms are used to describe gene function in biology and bio-medicine. |
| Approach: | They propose a task to generate term names for GO and build a large-scale benchmark dataset. |
| Outcome: | The proposed model outperforms baselines by incorporating the relations between genes, words and terms for term name generation. |