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

• URL: http://arxiv.org/abs/2311.00233v2
• Date: Sat, 17 Feb 2024 09:00:29 GMT
• Title: Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions
• Authors: Taehyeon Kim, Joonkee Kim, Gihun Lee, Se-Young Yun
• Abstract summary: This paper presents Instructive Decoding (ID), a simple yet effective approach that augments the efficacy of instruction-tuned models. ID adjusts the logits for next-token prediction in a contrastive manner, utilizing predictions generated from a manipulated version of the original instruction. 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 deviated responses.
• Score: 26.192531184689763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While instruction-tuned language models have demonstrated impressive zero-shot generalization, these models often struggle to generate accurate responses when faced with instructions that fall outside their training set. This paper presents Instructive Decoding (ID), a simple yet effective approach that augments the efficacy of instruction-tuned models. Specifically, ID adjusts the logits for next-token prediction in a contrastive manner, utilizing predictions generated from a manipulated version of the original instruction, referred to as a noisy instruction. This noisy instruction aims to elicit responses that could diverge from the intended instruction yet remain plausible. 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 achieves considerable performance gains across various instruction-tuned models and tasks without necessitating any additional parameter updates. Notably, utilizing 'opposite' as the noisy instruction in ID, which exhibits the maximum divergence from the original instruction, consistently produces the most significant performance gains across multiple models and tasks.

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