Contrastive Instruction Tuning
Tianyi Yan Fei Wang James Y. Huang Wenxuan Zhou Fan Yin Aram Galstyan Wenpeng Yin Muhao Chen
Instruction tuning has been used as a promising approach to improve the
performance of large language models (LLMs) on unseen tasks. However, current
LLMs exhibit limited robustness to unseen instructions, generating inconsistent
outputs when the same instruction is phrased with slightly varied forms or
language styles. This behavior indicates LLMs' lack of robustness to textual
variations and generalizability to unseen instructions, potentially leading to
trustworthiness issues. Accordingly, we propose Contrastive Instruction Tuning,
which maximizes the similarity between the hidden representations of
semantically equivalent instruction-instance pairs while minimizing the
similarity between semantically different ones. To facilitate this approach, we
augment the existing FLAN collection by paraphrasing task instructions.
Experiments on the PromptBench benchmark show that CoIN consistently improves
LLMs' robustness to unseen instructions with variations across character, word,
sentence, and semantic levels by an average of +2.5% in accuracy.