Summary of Logic Distillation: Learning From Code Function by Function For Planning and Decision-making, By Dong Chen et al.
Logic Distillation: Learning from Code Function by Function for Planning and Decision-making
by Dong Chen, Shilin Zhang, Fei Gao, Yueting Zhuang, Siliang Tang, Qidong Liu, Mingliang Xu
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach called Logic Distillation (LD) is proposed to empower smaller Large Language Models (S-LLMs) with the logical reasoning capabilities of larger LLMs. Typically, S-LLMs that can be deployed on various devices exhibit inferior performance compared to paid interfaces hosting larger LLMs. Knowledge distillation aims to bridge this gap by having S-LLMs mimic the outputs of L-LLMs, but this method does not provide powerful logical reasoning capabilities. LD tackles this challenge by first using large LLMs to instantiate complex instructions into discrete functions and then fine-tuning S-LLMs based on these functions. This approach enables S-LLMs to learn the logic employed by large LLMs in planning and decision-making tasks, achieving outstanding results comparable to or surpassing those of large LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Smaller Language Models are great at doing many things, but they’re not very good at making decisions and planning. This is because they don’t have the same level of understanding as larger models that require expensive computers. Researchers tried to fix this by having smaller models copy what the bigger ones do, but it didn’t work well enough. To solve this problem, a new method called Logic Distillation was created. It works by first using the big models to break down complex instructions into simple steps and then teaching the small models how to follow those steps. This allows the small models to make good decisions and plan effectively, just like the big ones. |
Keywords
* Artificial intelligence * Distillation * Fine tuning * Knowledge distillation
