English(EN) A Systematic Investigation of Bias in Large Language Models for Advertising Relevance

研究发现LLM在广告相关性方面存在偏见，影响GPT-4o和Qwen-7B

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

一项新近发表在arXiv上的研究，调查了大型语言模型（LLMs）在用于广告相关性评估时可能存在的偏见。研究人员检查了GPT-4o和Qwen-7B，发现改变广告商身份或输入语言会改变相关性评估。研究还揭示了LLM在就业、住房和信贷相关查询中的判断存在人口统计学刻板印象，尤其是在性别和职业方面。研究探讨了模型推理和训练过程中的缓解策略，其有效性取决于广告商信息的时效性和训练数据的分布。 AI

影响强调了LLM驱动的广告系统中潜在的公平性风险，敦促开发者实施缓解策略。

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arXiv cs.AI TIER_1 English(EN) · Weiwei Wang, Yinchuan Xu, Jialu Gao, Youkow Homma, Jian Jiao · 2026-10-07 04:00

大型语言模型在广告相关性方面偏差的系统性调查

arXiv:2610.07544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to judge how well an advertisement matches a query, but the fairness of these judgments has received limited attention. We conduct a systematic study of fairness in relevance judgme…

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大型语言模型在广告相关性方面偏差的系统性调查

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