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

5 Aug 2024arXiv:2408.02442archive 2025-07-28

Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-Yi Lee, Yun-Nung Chen

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.

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check_text_format appier-research/structure-gen/visualize.py official repository ran · violated contract no licence file found · pointer only · 50d9cff245c4ee69 · report
extract_acc appier-research/structure-gen/visualize.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 3430456fa69840c3 · report
extract_task appier-research/structure-gen/visualize.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · cd1054555f637651 · report

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