Reframing Data Value for Large Language Models Through the Lens of Plausibility
Mohamad Rida Rammal, Ruida Zhou, Suhas Diggavi
TL;DR
This work proposes an alternative perspective on the data value problem for language models, centering around the plausibility of the data, and posit that data holds lesser value if it can be plausibly generated by the model itself.
Abstract
Data valuation seeks to answer the important question, "How much is this data worth?" Existing data valuation methods have largely focused on discriminative models, primarily examining data value through the lens of its utility in training. However, with the push for ever-larger language models, relying on valuation methods that require training becomes increasingly expensive and dependent on specific techniques. We propose an alternative perspective on the data value problem for language models, centering around the plausibility of the data. We posit that data holds lesser value if it can be plausibly generated by the model itself. Starting from some intuitive criteria that align with our notions of valuable data, we develop a novel value function that is computationally tractable and derived from first principles with provable properties. We conduct a theoretical analysis of our value function and evaluate it across multiple scenarios and datasets.