# Does Prompt Formatting Have Any Impact on LLM Performance?

**Authors:** Jia He, Mukund Rungta, David Koleczek, Arshdeep Sekhon, Franklin X Wang, Sadid Hasan  
**arXiv:** [2411.10541](https://arxiv.org/abs/2411.10541)  
**Date:** 2024-11-15  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2411.10541v1/

## Key points

- Prompt optimization is crucial for determining Large Language Models (LLMs) performance
- Previous research has explored rephrasing prompt contexts and different prompting techniques like in-context learning and chain-of-thought
- Limited understanding of LLM sensitivity to prompt templates
- Investigating impact of different prompt templates on LLM performance
- Experiment involved formatting identical contexts into diverse human-readable templates (plain text, Markdown, JSON, YAML)
- Significant variations in performance among models observed
- Larger models like GPT-4 showed more robustness to template variations
- Different architectures within the same family of GPT models react differently to identical prompts
- No single prompt format universally excels across various GPT models
- Authors advocate for incorporating diverse prompt formats in future LLM testing for accurate assessment and enhancement of model performance
- Model size influences responses to prompts and prompts further exploration into explainability practices within large language models

## Summary

In the realm of Large Language Models (LLMs), prompt optimization plays a crucial role in determining model performance. Previous research has explored various aspects such as rephrasing prompt contexts and employing different prompting techniques like in-context learning and chain-of-thought. Additionally, organizing few-shot examples has also been studied. However, our understanding of LLM sensitivity to prompt templates remains limited. This paper aims to address this gap by investigating the impact of different prompt templates on LLM performance. The study involved formatting identical contexts into diverse human-readable templates, including plain text, Markdown, JSON, and YAML. These templates were then evaluated across tasks such as natural language reasoning, code generation, and translation using OpenAI's GPT models. The experiments revealed significant variations in performance among the different models. For instance,<kg> GPT-3.5-turbo </kg> showed up to a 40% fluctuation in a code translation task based on the chosen prompt template. Interestingly,<kg> larger models like GPT-4 </kg> exhibited more robustness to these template variations. This highlights the importance of reconsidering fixed prompt templates due to their substantial impact on model performance. It was observed that even within the same family of GPT models,<kg> different architectures react differently to identical prompts </kg>. This underscores the need for tailored prompt engineering specific to each model for optimal performance. In conclusion,<kg> no single prompt format universally excels across various GPT models </kg>. This challenges current evaluation methods that often overlook prompt structure and may lead to misjudgments regarding a model's true capabilities. The authors advocate for incorporating diverse prompt formats in future LLM testing to accurately assess and enhance model performance. Furthermore,<kg> the study also touches upon how model size influences responses to prompts </kg> and prompts further exploration into explainability practices within large language models with extensive context windows.