Scholarlink

This paper focuses on bias caused by changing personal names in the input of proprietary LLMs, fine-tuned language models, and lexicon-based sentiment analysis tool. It extracts examples from NLI and story cloze task datasets with personal names, replaces them with names of celebrities, ordinary people or mixtures. Results show that altered names significantly influence model assessments, especially with more than two choices, indicating potential real-world application hazards. A mitigation method for “fame bias” is proposed but the problem requires further research.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fame Bias – Large Language Models Change Their Judgement Depending on Personal Name

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Huizhong Ji,
  • Rafal Rzepka

摘要

This paper focuses on bias caused by changing personal names in the input of proprietary LLMs, fine-tuned language models, and lexicon-based sentiment analysis tool. It extracts examples from NLI and story cloze task datasets with personal names, replaces them with names of celebrities, ordinary people or mixtures. Results show that altered names significantly influence model assessments, especially with more than two choices, indicating potential real-world application hazards. A mitigation method for “fame bias” is proposed but the problem requires further research.