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This study dissects how presentation factors like bold text and vocabulary diversity influence human voting in the French Compar:IA LLM arena, revealing that formatting cues significantly skew raw rankings without necessarily reflecting underlying model capability. The authors find that adjusting for these stylistic features shifts 36 models by over ten ranks, suggesting a need to publish both raw and adjusted results for transparent sensitivity analysis.
What Wins a Vote? Formatting, Length, and Lexical Diversity in the French Compar:IA LLM Arena
arXiv:2610.01316v1 Announce Type: new Abstract: LLM arenas turn pairwise human preferences into model rankings. Those preferences may reflect how an answer is presented as well as what it says. We take a stylometric approach to 137,293 decisive French-language votes from the July 2026 Compar:IA release; the primary formatting analysis includes 137,113 battles across 116 models, and the joint estimates use the 127,092 battles with all required measurements. For each battle, we reconstruct the response visible when the user voted. We then compare the raw ranking with rankings adjusted for formatting, length, readability, vocabulary variety, and sentence structure. Presentation is associated with winning, but length, bold text, and lists tend to occur together, making their individual contributions hard to separate. Across the measured features, two associations change least across specifications: bold usage (+11.0% win odds per standard deviation in the joint model) and moving-average type-token ratio (MATTR), a measure of vocabulary variety that is less sensitive to answer length (+16.8%). The bold association is substantially smaller in observed multi-turn conversations, whereas the MATTR association changes little; because users choose whether to continue, this difference is descriptive rather than causal. The full adjustment moves 36 of 116 models by at least ten ranks. Yet comparisons with external benchmarks do not show that adjusted rankings better measure capability. We therefore recommend publishing raw and adjusted rankings side by side as a transparent sensitivity analysis.
Title:What Wins a Vote? Formatting, Length, and Lexical Diversity in the French Compar:IA LLM Arena
Abstract:LLM arenas turn pairwise human preferences into model rankings. Those preferences may reflect how an answer is presented as well as what it says. We take a stylometric approach to 137,293 decisive French-language votes from the July 2026 Compar:IA release; the primary formatting analysis includes 137,113 battles across 116 models, and the joint estimates use the 127,092 battles with all required measurements. For each battle, we reconstruct the response visible when the user voted. We then compare the raw ranking with rankings adjusted for formatting, length, readability, vocabulary variety, and sentence structure. Presentation is associated with winning, but length, bold text, and lists tend to occur together, making their individual contributions hard to separate. Across the measured features, two associations change least across specifications: bold usage (+11.0% win odds per standard deviation in the joint model) and moving-average type-token ratio (MATTR), a measure of vocabulary variety that is less sensitive to answer length (+16.8%). The bold association is substantially smaller in observed multi-turn conversations, whereas the MATTR association changes little; because users choose whether to continue, this difference is descriptive rather than causal. The full adjustment moves 36 of 116 models by at least ten ranks. Yet comparisons with external benchmarks do not show that adjusted rankings better measure capability. We therefore recommend publishing raw and adjusted rankings side by side as a transparent sensitivity analysis.
Computation and Language (cs.CL)
arXiv:2610.01316 [cs.CL]
(or arXiv:2610.01316v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.01316 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.5281/zenodo.23043602 Focus to learn more DOI(s) linking to related resources
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