Summary of Beyond Answers: Transferring Reasoning Capabilities to Smaller Llms Using Multi-teacher Knowledge Distillation, by Yijun Tian et al.
Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation
by Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Transferring knowledge from larger language models (LLMs) to smaller ones is a desirable goal, as it enables more flexible and cost-effective deployment. Knowledge distillation has been shown to be an efficient method for achieving this, but existing approaches have limitations such as limited knowledge diversity and lack of contextual information. To address these issues, we propose TinyLLM, a novel knowledge distillation paradigm that learns a small student LLM from multiple large teacher LLMs. Our approach encourages the student model not only to generate correct answers but also to understand the rationales behind those answers. We use an in-context example generator and a teacher-forcing Chain-of-Thought strategy to ensure accurate and contextually grounded rationales. Extensive experiments on six datasets across two reasoning tasks demonstrate the superiority of our method, with TinyLLM outperforming large teacher LLMs despite being significantly smaller. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a small language model that can understand and respond to questions. This is great for chatbots or virtual assistants, but it’s hard to teach these models new things because they’re so small. To solve this problem, we’ve developed a way to transfer knowledge from bigger language models to smaller ones. Our method, called TinyLLM, lets the small model learn not just what answers are correct, but also why those answers make sense. This helps the small model understand the context of the question and give better responses. We tested our method on six different datasets and it worked really well, even beating the bigger models in some cases! |
Keywords
* Artificial intelligence * Knowledge distillation * Language model * Student model
