Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions

ACL ARR 2025 February Submission1725 Authors

Abstract: With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instructions during ICL remains under-explored. In this work, we propose an ensemble prompt framework to describe the selection criteria of multiple in-context examples, and preliminary experiments on machine translation (MT) across six translation directions confirm that this framework boosts ICL performance. But to our surprise, LLMs might not care what the descriptions actually say, and the performance gain is primarily caused by the ensemble format, since it could lead to improvement even with random descriptive nouns. We further apply this new ensemble framework on a range of commonsense, math, logical reasoning and hallucination tasks with three LLMs and achieve promising results, suggesting again that designing a proper prompt format would be much more effective and efficient than paying effort into specific descriptions. Our code will be publicly available once this paper is published.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: prompting, applications

Contribution Types: Model analysis & interpretability

Languages Studied: English, German, French, Russian

Submission Number: 1725