Papers for

content editors

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Presentation style and vocabulary diversity influence French AI vote outcomes

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.

Thu 1 OctComputation and Language
The gist
When people vote between answers from different AI models in French, how the answers look and the words they use affect which answer wins. The authors studied over 137,000 votes and found that answers with bold text and a wider variety of words tend to be chosen more often. However, these factors don’t always predict the AI’s actual ability, so both original and adjusted rankings should be shown. This helps understand how presentation affects opinions without confusing it with actual quality.
Open → 2610.01316v1

Language models can prematurely settle writer’s organizing problems

Early Epistemic Settlement in AI-Assisted Writing

Abstract: A language model can resolve a writer's current organizing problem while the construction needed for her own resolution remains unfinished. I call this early epistemic settlement. The supplied organization meets every demand then governing the passage, yet proceeding from it can displace work through which the writer would have changed those demands or become able to form further organizations. I distinguish the coordination needed to complete an already formable organization from construction that changes which organizations are formable in the first place. Settlement in the first case changes the relative work still required to bring available organizations to sufficiency. In the second, it can remove the need for the work through which another organization would become formable. Even when supplied resolution and continued construction leave the same visible qualification, different dependencies in the writer's inquiry may support different later organizations. Model suggestions can also contribute to this development when writers work through them while the problem remains unresolved. In theoretical and exploratory writing, the relations developed in reaching local adequacy help determine what the writer can later defend or develop. A sound judgment that the present passage is sufficient can therefore make further inquiry dispensable before that generative work has occurred.

Tue 8 SeptHuman-Computer InteractionComputers and Society
The gist
Writers sometimes use AI language models to help solve problems in their writing before fully finishing their own ideas. The authors explain how accepting these AI suggestions too soon can stop writers from exploring better ideas or changing their original plans. Even if the writing looks good at the moment, this early agreement with the AI can limit future changes or deeper thinking. This is especially true for complex writing, like theoretical or exploratory texts.
Open → 2609.09332v1