Summary of Learning to Maximize Mutual Information For Chain-of-thought Distillation, by Xin Chen et al.
Learning to Maximize Mutual Information for Chain-of-Thought Distillation
by Xin Chen, Hanxian Huang, Yanjun Gao, Yi Wang, Jishen Zhao, Ke Ding
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Knowledge distillation, a crucial technique for efficient AI deployment, has seen significant advancements with the introduction of Distilling Step-by-Step (DSS), a novel method leveraging chain-of-thought (CoT) distillation. DSS allows smaller models to acquire superior reasoning capabilities from their larger counterparts by generating rationales and predicting labels concurrently through multi-task learning. However, this approach overlooks the intrinsic relationship between task training, leading to ineffective knowledge integration. This paper investigates the mutual relationship of tasks from an Information Bottleneck perspective, formulating it as maximizing the mutual information of representation features. A variational approach is proposed to solve this optimization problem using a learning-based method. Experimental results across four datasets demonstrate that our method outperforms state-of-the-art DSS, offering valuable insights for future research on language model distillation and CoT applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a super smart AI model and teaching a smaller one how to think like it. This process is called knowledge distillation. Researchers have developed a new way to do this called Distilling Step-by-Step (DSS). It works by giving the small model two jobs: come up with reasons why something is true, and predict what category something belongs in. The problem is that these tasks are connected, but DSS doesn’t account for this. This paper figures out how to make DSS better by understanding how these tasks relate to each other. They tested it on four different sets of data and found that their new method works even better than the old one. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation » Language model » Multi task » Optimization
