Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models

Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee +1 more
8/5/2024
cs.CL

No official code found. Best implementation: appier-research/structure-gen (29 stars, last commit 2025-05-31).

Abstract

Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs). This study investigates whether such constraints on generation space impact LLMs abilities, including reasoning and domain knowledge comprehension. Specifically, we evaluate LLMs performance when restricted to adhere to structured formats versus generating free-form responses across various common tasks. Surprisingly, we observe a significant decline in LLMs reasoning abilities under format restrictions. Furthermore, we find that stricter format constraints generally lead to greater performance degradation in reasoning tasks.

View on arXivView PDF

Code Implementations(1)

Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models

295Shell, PythonSep 13, 20241 years ago
json-modelarge-language-modellarge-language-modelsllmstructure-gen

Cite this paper

@article{tam2024speak,
  title  = {Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models},
  author = {Zhi Rui Tam and Cheng-Kuang Wu and Yi-Lin Tsai and Chieh-Yen Lin and Hung-yi Lee and Yun-Nung Chen},
  year   = {2024},
  eprint = {2408.02442},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.02442v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2408.02442.svg)](https://codeofpaper.com/paper/2408.02442)

Discussion