Explanation-based Finetuning Makes Models More Robust to Spurious Cues

Understand

Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data.

• We propose explanation-based finetuning as a general approach to mitigate LLMs' reliance on spurious correlations.
• Unlike standard finetuning where the model only predicts the answer given the input, we finetune the model to additionally generate a free-text explanation supporting its answer.
• To evaluate our method, we finetune the model on artificially constructed training sets containing different types of spurious cues, and test it on a test set without these cues.