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

2024-08-16

arXiv.org

AI Summary

This study challenges the assumed necessity of descriptive instructions in large language model (LLM) in-context learning (ICL), questioning whether their role has been overestimated. Through systematic ablation experiments, we find that performance gains primarily stem from the structured formatting of prompts—not their semantic content. To formalize this insight, we propose the Random Noun Ensemble (RNE) framework: it preserves the syntactic structure of instruction templates while substituting semantically irrelevant random nouns for original instruction terms—yet consistently improves model performance. RNE thus decouples prompt efficacy from semantic coherence, overturning the conventional paradigm of instruction engineering grounded in linguistic plausibility. Evaluated across six-way machine translation, commonsense/mathematical/logical reasoning, and hallucination detection benchmarks, RNE significantly enhances the performance of three major LLM families, demonstrating the universality and efficiency of format-driven prompt design.

Technology Category

Natural Language Processing: Prompt Engineering / PromptingMachine Learning: Large Multimodal Models (LMMs)Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Explores impact of descriptive instructions in ICL
• Proposes ensemble prompt framework for ICL
• Tests framework on diverse LLM tasks

Innovation

• Ensemble prompt framework
• In-context learning enhancement
• Format over description effectiveness