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        <title>Lost in Formatting: How Output Formats Skew LLM Performance on Information Extraction</title>
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    <abstract>We investigate how the choice of output format influences the performance of fine-tuned large language models on information extraction tasks. Based on over 280 experiments spanning multiple benchmarks, models and formats, we find that output formatting is a critical yet largely overlooked hyperparameter. Remarkably, in some cases, changing only the output format shifts F1 scores by over 40% despite using the same model. We further observe that no single format consistently dominates across settings, and the optimal choice depends on factors like model family and dataset characteristics. Overall, these results demonstrate that informationally equivalent output formats can produce substantial performance variation, highlighting the need to treat output formatting as a key factor in building accurate and reliable information extraction systems.</abstract>
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%0 Conference Proceedings
%T Lost in Formatting: How Output Formats Skew LLM Performance on Information Extraction
%A Ravikumar, Rishi
%A Ibrahim, Nuhu
%A Batista-Navarro, Riza
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F ravikumar-etal-2026-lost
%X We investigate how the choice of output format influences the performance of fine-tuned large language models on information extraction tasks. Based on over 280 experiments spanning multiple benchmarks, models and formats, we find that output formatting is a critical yet largely overlooked hyperparameter. Remarkably, in some cases, changing only the output format shifts F1 scores by over 40% despite using the same model. We further observe that no single format consistently dominates across settings, and the optimal choice depends on factors like model family and dataset characteristics. Overall, these results demonstrate that informationally equivalent output formats can produce substantial performance variation, highlighting the need to treat output formatting as a key factor in building accurate and reliable information extraction systems.
%U https://aclanthology.org/2026.eacl-long.256/
%P 5498-5513

Markdown (Informal)

[Lost in Formatting: How Output Formats Skew LLM Performance on Information Extraction](https://aclanthology.org/2026.eacl-long.256/) (Ravikumar et al., EACL 2026)

• Lost in Formatting: How Output Formats Skew LLM Performance on Information Extraction (Ravikumar et al., EACL 2026)

ACL

• Rishi Ravikumar, Nuhu Ibrahim, and Riza Batista-Navarro. 2026. Lost in Formatting: How Output Formats Skew LLM Performance on Information Extraction. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5498–5513, Rabat, Morocco. Association for Computational Linguistics.