I Learn Better If You Speak My Language: Understanding The Superior Performance Of Fine-tuning Large Language Models With Llm-generated Responses
Ren Xuan, Wu Biao, Liu Lingqiao. Arxiv 2024
[Paper]
This paper explores an intriguing observation: fine-tuning a large language model (LLM) with responses generated by a LLM often yields better results than using responses generated by humans. We conduct an in-depth investigation to understand why this occurs. Contrary to the common belief that these instances is simply due to the more detailed nature of LLM-generated content, our study identifies another contributing factor: an LLM is inherently more “familiar” with LLM generated responses. This familiarity is evidenced by lower perplexity before fine-tuning. We design a series of experiments to understand the impact of the “familiarity” and our conclusion reveals that this “familiarity” significantly impacts learning performance. Training with LLM-generated responses not only enhances performance but also helps maintain the model’s capabilities in other tasks after fine-tuning on a specific task.
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