March 14, 2024, 4:43 a.m. | Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu

cs.LG updates on

arXiv:2403.08743v1 Announce Type: cross
Abstract: Large language models (LLMs) can easily generate biased and discriminative responses. As LLMs tap into consequential decision-making (e.g., hiring and healthcare), it is of crucial importance to develop strategies to mitigate these biases. This paper focuses on social bias, tackling the association between demographic information and LLM outputs. We propose a causality-guided debiasing framework that utilizes causal understandings of (1) the data-generating process of the training corpus fed to LLMs, and (2) the internal reasoning …

abstract arxiv association bias biases causality cs.lg decision framework generate healthcare hiring importance information language language models large language large language models llms making paper responses social strategies type unbiased

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