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        <title>TitleTrap: Probing Presentation Bias in LLM-Based Scientific Reviewing</title>
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            <namePart type="given">Mousumi</namePart>
            <namePart type="family">Akter</namePart>
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            <namePart type="given">Tahiya</namePart>
            <namePart type="family">Chowdhury</namePart>
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    <abstract>Large language models (LLMs) are now used in scientific peer review, but their judgments can still be influenced by how information is presented. We study how the style of a paper’s title affects the way LLMs score scientific work. To control for content variation, we build the TitleTrap benchmark using abstracts generated by a language model for common research topics in computer vision and NLP. Each abstract is paired with three titles: a branded colon style, a plain descriptive style, and an interrogative style, while the abstract text remains fixed. We ask GPT-4o and Claude to review these title–abstract pairs under the same instructions. Our results show that title style alone can change the scores: branded titles often receive higher ratings, while interrogative titles sometimes lead to lower assessments of rigor. These findings reveal a presentation bias in LLM-based peer review and suggest the need for better methods to reduce such bias and support fairer automated evaluation.</abstract>
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            <start>119</start>
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%0 Conference Proceedings
%T TitleTrap: Probing Presentation Bias in LLM-Based Scientific Reviewing
%A Du, Shurui
%Y Akter, Mousumi
%Y Chowdhury, Tahiya
%Y Eger, Steffen
%Y Leiter, Christoph
%Y Opitz, Juri
%Y Çano, Erion
%S Proceedings of the 5th Workshop on Evaluation and Comparison of NLP Systems
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-305-0
%F du-2025-titletrap
%X Large language models (LLMs) are now used in scientific peer review, but their judgments can still be influenced by how information is presented. We study how the style of a paper’s title affects the way LLMs score scientific work. To control for content variation, we build the TitleTrap benchmark using abstracts generated by a language model for common research topics in computer vision and NLP. Each abstract is paired with three titles: a branded colon style, a plain descriptive style, and an interrogative style, while the abstract text remains fixed. We ask GPT-4o and Claude to review these title–abstract pairs under the same instructions. Our results show that title style alone can change the scores: branded titles often receive higher ratings, while interrogative titles sometimes lead to lower assessments of rigor. These findings reveal a presentation bias in LLM-based peer review and suggest the need for better methods to reduce such bias and support fairer automated evaluation.
%U https://aclanthology.org/2025.eval4nlp-1.10/
%P 119-125

Markdown (Informal)

[TitleTrap: Probing Presentation Bias in LLM-Based Scientific Reviewing](https://aclanthology.org/2025.eval4nlp-1.10/) (Du, Eval4NLP 2025)

• TitleTrap: Probing Presentation Bias in LLM-Based Scientific Reviewing (Du, Eval4NLP 2025)

ACL

• Shurui Du. 2025. TitleTrap: Probing Presentation Bias in LLM-Based Scientific Reviewing. In Proceedings of the 5th Workshop on Evaluation and Comparison of NLP Systems, pages 119–125, Mumbai, India. Association for Computational Linguistics.