Papers by Fuwei Zhang
An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical Reasoning (2025.acl-long)
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| Challenge: | Existing methods for constructing process supervision training data are costly or suffer from poor quality. |
| Approach: | They propose a framework called EpicPRM which annotates each intermediate reasoning step based on its quantified contribution and uses an adaptive binary search algorithm to enhance annotation precision and efficiency. |
| Outcome: | The proposed framework improves annotation precision and efficiency and can be used to train a high-quality training dataset with 50k annotated intermediate steps. |
HierGR: Hierarchical Semantic Representation Enhancement for Generative Retrieval in Food Delivery Search (2025.acl-industry)
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Fuwei Zhang, Xiaoyu Liu, Xinyu Jia, Yingfei Zhang, Zenghua Xia, Fei Jiang, Fuzhen Zhuang, Wei Lin, Zhao Zhang
| Challenge: | Generative retrieval (GR) is an emerging search paradigm for food delivery search. |
| Approach: | They propose a method that harnesses the advanced query understanding capabilities of large language models to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios. |
| Outcome: | The proposed method increases the number of online orders by 0.68% for complex search intents. |
Multi-level Relevance Document Identifier Learning for Generative Retrieval (2025.acl-long)
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Fuwei Zhang, Xiaoyu Liu, Xinyu Jia, Yingfei Zhang, Shuai Zhang, Xiang Li, Fuzhen Zhuang, Wei Lin, Zhao Zhang
| Challenge: | Existing methods generate DocIDs based on textual content, which may result in weak semantic connections for similar documents due to variations in expression. |
| Approach: | They propose a new retrieval paradigm that generates unique document identifiers . they propose to use queries as a bridge to connect documents with varying relevance levels . |
| Outcome: | The proposed approach outperforms existing methods on multilingual e-commerce search datasets. |
Should We Use a Fixed Embedding Size? Customized Dimension Sizes for Knowledge Graph Embedding (2025.coling-main)
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| Challenge: | Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, which is crucial for knowledge completion, fusion, and inference. |
| Approach: | They propose to embed entities and relations into a low-dimensional space to enable knowledge Graphs to be effectively used by downstream AI tasks. |
| Outcome: | The proposed framework is universal and flexible, suitable for various KGE models. |