In-Context Probing Approximates Influence Function for Data Valuation

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• github.com/cxcscmu/InContextDataValuation
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Abstract

Data valuation quantifies the value of training data, and is used for data attribution (i.e., determining the contribution of training data towards model predictions), and data selection; both of which are important for curating high-quality datasets to train large language models. In our paper, we show that data valuation through in-context probing (i.e., prompting a LLM) approximates influence functions for selecting training data. We provide a theoretical sketch on this connection based on transformer models performing "implicit" gradient descent on its in-context inputs. Our empirical findings show that in-context probing and gradient-based influence frameworks are similar in how they rank training data. Furthermore, fine-tuning experiments on data selected by either method reveal similar model performance.

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