Papers by Zhijie Nie
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. |
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. |
Dynamic Task Vector Grouping for Efficient Multi-Task Prompt Tuning (2025.findings-acl)
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| Challenge: | Existing approaches transfer the soft prompt to low-source targets by combining all source tasks or a single “high-similar” source task one-time-only. |
| Approach: | They propose a method to group similar source tasks based on two metrics: target similarity and knowledge consistency. |
| Outcome: | The proposed method reduces negative transfer and improves performance on low-source targets. |