BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Models

• URL: http://arxiv.org/abs/2510.00307v1
• Date: Tue, 30 Sep 2025 22:02:13 GMT
• Title: BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Models
• Authors: Thierry Blankenstein, Jialin Yu, Zixuan Li, Vassilis Plachouras, Sunando Sengupta, Philip Torr, Yarin Gal, Alasdair Paren, Adel Bibi,
• Abstract summary: Large language models (LLMs) often rely on external tools drawn from marketplaces where multiple providers offer functionally equivalent options.<n>This raises a critical point concerning fairness: if selection is systematically biased, it can degrade user experience and distort competition.<n>We introduce a benchmark of diverse tool categories, each containing multiple functionally equivalent tools, to evaluate tool-selection bias.
• Score: 55.119657444627855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agents backed by large language models (LLMs) often rely on external tools drawn from marketplaces where multiple providers offer functionally equivalent options. This raises a critical point concerning fairness: if selection is systematically biased, it can degrade user experience and distort competition by privileging some providers over others. We introduce a benchmark of diverse tool categories, each containing multiple functionally equivalent tools, to evaluate tool-selection bias. Using this benchmark, we test seven models and show that unfairness exists with models either fixating on a single provider or disproportionately preferring earlier-listed tools in context. To investigate the origins of this bias, we conduct controlled experiments examining tool features, metadata (name, description, parameters), and pre-training exposure. We find that: (1) semantic alignment between queries and metadata is the strongest predictor of choice; (2) perturbing descriptions significantly shifts selections; and (3) repeated pre-training exposure to a single endpoint amplifies bias. Finally, we propose a lightweight mitigation that first filters the candidate tools to a relevant subset and then samples uniformly, reducing bias while preserving good task coverage. Our findings highlight tool-selection bias as a key obstacle for the fair deployment of tool-augmented LLMs.

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