Clear Minds Think Alike: What Makes LLM Fine-tuning Robust? A Study of Token Perplexity

• URL: http://arxiv.org/abs/2501.14315v1
• Date: Fri, 24 Jan 2025 08:18:56 GMT
• Title: Clear Minds Think Alike: What Makes LLM Fine-tuning Robust? A Study of Token Perplexity
• Authors: Chao-Chung Wu, Zhi Rui Tam, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen,
• Abstract summary: We show that fine-tuning with LLM-generated data improves target task performance and reduces out-of-domain degradation. This is the first mechanistic explanation for the superior OOD robustness conferred by LLM-generated training data.
• Score: 61.48338027901318
• License:
• Abstract: Maintaining consistent model performance across domains is a fundamental challenge in machine learning. While recent work has explored using LLM-generated data for fine-tuning, its impact on cross-domain generalization remains poorly understood. In this paper, we present a systematic analysis revealing that fine-tuning with LLM-generated data not only improves target task performance but also reduces out-of-domain (OOD) degradation compared to fine-tuning with ground truth data. Through analyzing the data sequence in tasks of various domains, we demonstrate that this enhanced OOD robustness stems from a reduced prevalence of high perplexity tokens in LLM-generated sequences. Following this hypothesis we showed that masking high perplexity tokens in ground truth training data also achieves similar OOD preservation comparable to using LLM-generated data. Extensive experiments across diverse model architectures and scales, including Gemma2-2B, Mistral-7B and Llama3-8B, corroborate the consistency of our findings. To the best of our knowledge, this work provides the first mechanistic explanation for the superior OOD robustness conferred by LLM-generated training data, offering valuable insights for developing more robust fine-tuning strategies.

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