Papers by Jishen Zhao
Learning to Maximize Mutual Information for Chain-of-Thought Distillation (2024.findings-acl)
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| Challenge: | Knowledge distillation is a technique of transferring knowledge from large, complex models to smaller ones. |
| Approach: | They propose a method utilizing chain-of-thought distillation to transfer knowledge from large, complex models to smaller ones by maximizing mutual information of the representation features of the two tasks. |
| Outcome: | The proposed method outperforms the state-of-the-art knowledge distillation method on four datasets. |
You Only Use Reactive Attention Slice When Retrieving From Long Context (2025.findings-emnlp)
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| Challenge: | Existing retrieval techniques for language models are limited due to their reliance on lexical similarity and are computationally expensive to train. |
| Approach: | They propose a training-free and fine-tuning-free attention-based retrieval technique that uses a reaction score heuristic to quantify how an LM’s self-attention “reacts” to a user query. |
| Outcome: | The proposed approach improves QA task accuracy by 15% and inference throughput by 31% compared to embedding-based retrieval. |