B-score: Detecting biases in large language models using response
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Authors: name: An Vo, name: Mohammad Reza Taesiri, name: Daeyoung Kim, name: Anh Totti Nguyen

Large language models (LLMs) often exhibit strong biases, e.g, against women
or in favor of the number 7. We investigate whether LLMs would be able to
output less biased answers when allowed to observe their prior answers to the
same question in a multi-turn conversation. To understand which types of
questions invite more biased answers, we test LLMs on our proposed set of
questions that span 9 topics and belong to three types: (1) Subjective; (2)
Random; and (3) Objective. Interestingly, LLMs are able to "de-bias" themselves
in a multi-turn conversation in response to questions that seek an Random,
unbiased answer. Furthermore, we propose B-score, a novel metric that is
effective in detecting biases to Subjective, Random, Easy, and Hard questions.
On MMLU, HLE, and CSQA, leveraging B-score substantially improves the
verification accuracy of LLM answers (i.e, accepting LLM correct answers and
rejecting incorrect ones) compared to using verbalized confidence scores or the
frequency of single-turn answers alone. Code and data are available at:
https://b-score.github.io.