Summary of I Learn Better If You Speak My Language: Understanding the Superior Performance Of Fine-tuning Large Language Models with Llm-generated Responses, by Xuan Ren and Biao Wu and Lingqiao Liu
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses
by Xuan Ren, Biao Wu, Lingqiao Liu
First submitted to arxiv on: 17 Feb 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 This paper investigates why fine-tuning a large language model (LLM) with responses generated by another LLM often produces better results than using human-generated responses, particularly in reasoning tasks. The study reveals that the key factor is not the level of detail in the generated content, but rather the LLM’s familiarity with its own responses, as measured by lower perplexity before fine-tuning. The authors design a series of experiments to examine the impact of this familiarity and conclude that it significantly improves learning performance, not only for the specific task being fine-tuned, but also for other reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into why using large language models (LLMs) to generate responses for another LLM can lead to better results. The researchers found that the main reason isn’t because the generated content is more detailed, but rather because the LLM is already familiar with its own responses. This familiarity makes a big difference in how well the model learns and performs on different tasks. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Perplexity
