Papers by Zhanyu Wu
A Text is Worth Several Tokens: Text Embedding from LLMs Secretly Aligns Well with The Key Tokens (2025.acl-long)
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| Challenge: | et al., 2023) show that text embeddings from large language models can be aligned with key tokens in input text. |
| Approach: | They propose a sparse retrieval method based on aligned tokens for large language models . they show that this phenomenon is universal and is not affected by model architecture . |
| Outcome: | The proposed method can achieve 80% of the dense retrieval effect of the same model while reducing the computation significantly. |
OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)
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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs. |
| Approach: | They propose a framework that integrates dialogue, reasoning, and personalized recommendation. |
| Outcome: | Experiments across public benchmarks show state-of-the-art performance. |
Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval (2026.acl-long)
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| Challenge: | Existing methods for dense retrieval use pseudo-relevance feedback to model dimension importance . however, they learn global transformations shared across queries and do not model dimension-aware dimension importance. |
| Approach: | They propose a Query-Aware Adaptive Dimension Selection framework that learns to predict per-dimension importance directly from query embedding. |
| Outcome: | The proposed framework improves retrieval effectiveness over the full-dimensional and PRF-based models. |