DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

• URL: http://arxiv.org/abs/2310.00902v3
• Date: Wed, 13 Mar 2024 14:27:46 GMT
• Title: DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
• Authors: Yongchan Kwon, Eric Wu, Kevin Wu, James Zou
• Abstract summary: We propose DataInf, an efficient influence approximation method that is practical for large-scale generative AI models. Our theoretical analysis shows that DataInf is particularly well-suited for parameter-efficient fine-tuning techniques such as LoRA. In applications to RoBERTa-large, Llama-2-13B-chat, and stable-diffusion-v1.5 models, DataInf effectively identifies the most influential fine-tuning examples better than other approximate influence scores.
• Score: 31.65198592956842
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influence function is a principled and popular data attribution method, but its computational cost often makes it challenging to use. This issue becomes more pronounced in the setting of large language models and text-to-image models. In this work, we propose DataInf, an efficient influence approximation method that is practical for large-scale generative AI models. Leveraging an easy-to-compute closed-form expression, DataInf outperforms existing influence computation algorithms in terms of computational and memory efficiency. Our theoretical analysis shows that DataInf is particularly well-suited for parameter-efficient fine-tuning techniques such as LoRA. Through systematic empirical evaluations, we show that DataInf accurately approximates influence scores and is orders of magnitude faster than existing methods. In applications to RoBERTa-large, Llama-2-13B-chat, and stable-diffusion-v1.5 models, DataInf effectively identifies the most influential fine-tuning examples better than other approximate influence scores. Moreover, it can help to identify which data points are mislabeled.

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