DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
ICLR 2024(2023)
摘要
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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关键词
Influence function,Data valuation
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