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While instruction-tuned language models have demonstrated impressivezero-shot generalization, these models often struggle to generate accurateresponses when faced with instructions that fall outside their training set.This paper presents Instructive Decoding (ID), a simple yet effective approachthat augments the efficacy of instruction-tuned models. Specifically, IDadjusts the logits for next-token prediction in a contrastive manner, utilizingpredictions generated from a manipulated version of the original instruction,referred to as a noisy instruction. This noisy instruction aims to elicitresponses that could diverge from the intended instruction yet remainplausible. We conduct experiments across a spectrum of such noisy instructions,ranging from those that insert semantic noise via random words to others like'opposite' that elicit the deviated responses. Our approach achievesconsiderable performance gains across various instruction-tuned models andtasks without necessitating any additional parameter updates. Notably,utilizing 'opposite' as the noisy instruction in ID, which exhibits the maximumdivergence from the original instruction, consistently produces the mostsignificant performance gains across multiple models and tasks.

Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions