Asking language models how to represent data for fine-tuning

Abstract

Language models are often used for tasks involving structured data like tables and graphs, but there is no principled approach for choosing the best format to represent such data for fine-tuning. We address this in three steps. First, we show that format choice remains important even after fine-tuning; models learn more efficiently with specific formats rather than adapting to any format. Second, we show that a pre-trained model can suggest its own candidate formats by auto-completing partial prompts, reducing reliance on developer intuition. Third, and most importantly, we demonstrate that base model performance across formats reliably predicts post-fine-tuning performance: the format that performs best before fine-tuning remains among the top candidates after fine-tuning in 16 out of 18 settings across three data structure types, three models, and six tasks. This finding allows format selection to be done via inference alone, avoiding costly trial-and-error fine-tuning runs.