Does Prompt Formatting Have Any Impact on LLM Performance?
It addresses the problem of prompt sensitivity for LLM users, showing that template choice can significantly impact performance, though it is incremental in exploring a known aspect of prompt optimization.
This paper investigates how different prompt templates (e.g., plain text, Markdown, JSON, YAML) affect LLM performance, finding that GPT-3.5-turbo's performance varies by up to 40% in code translation tasks, while GPT-4 is more robust.
In the realm of Large Language Models (LLMs), prompt optimization is crucial for model performance. Although previous research has explored aspects like rephrasing prompt contexts, using various prompting techniques (like in-context learning and chain-of-thought), and ordering few-shot examples, our understanding of LLM sensitivity to prompt templates remains limited. Therefore, this paper examines the impact of different prompt templates on LLM performance. We formatted the same contexts into various human-readable templates, including plain text, Markdown, JSON, and YAML, and evaluated their impact across tasks like natural language reasoning, code generation, and translation using OpenAI's GPT models. Experiments show that GPT-3.5-turbo's performance varies by up to 40\% in a code translation task depending on the prompt template, while larger models like GPT-4 are more robust to these variations. Our analysis highlights the need to reconsider the use of fixed prompt templates, as different formats can significantly affect model performance.