真实性的回声:在大型语言模型时代,恢复人类情感
Yifei Wang1, Ashkan Eshghi1, Yi Ding1
1Warwick Business School, University of Warwick, Scarman Road, Coventry, CV4 7AL, United Kingdom.
PNAS nexus
|February 26, 2025
概括
大型语言模型 (LLM) 编辑用户生成内容 (UGC) 可以中和情绪,扭曲研究. 本研究提出了检测和纠正LLM诱导的情绪偏差在UGC可靠分析的方法.
科学领域:
- 自然语言处理自然语言处理.
- 计算社会科学 计算社会科学
- 人工智能伦理学 人工智能伦理学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于编辑用户生成内容 (UGC).
- 法学士编辑可能无意中改变原始内容的情感.
- 对UGC的情感分析对于研究和政策制定至关重要.
研究的目的:
- 调查LLM编辑对UGC情绪的影响.
- 评估LLM诱导的情绪变化如何影响研究结果.
- 在UGC情绪分析中制定减轻LLM相关偏见的策略.
主要方法:
- 气候变化推特数据集的分析,由LLMs重新表述.
- 对有关气候变化的公众论研究的复制.
- 开发预测模型以识别原始的人类情绪.
- 微调LLM以使生成的文本情绪与人类情绪保持一致.
主要成果:
- 与原始推特相比,LLM重写的推特表现出更为中立的情绪.
- 由LLM引起的情绪变化可能会扭曲研究结果.
- 预测模型和LLM微调显示出纠正情绪偏差的希望.
结论:
- 对UGC的LLM编辑引入了可能影响研究完整性的情感偏见.
- 提出的方法,包括预测建模和LLM微调,可以帮助减轻这些偏见.
- 在研究中确保情绪分析的可靠性,需要解决LLM引起的UGC变化.
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