根据LLMs的情感内涵:对意义测量和文化偏见的含义
Aidan Combs1,2, Diego Dametto3,4, Christophe Blaison5
1Chair for Fundamentals of Natural Language Processing, University of Bamberg, Bamberg, Germany.
Cognition & emotion
|October 12, 2025
概括
生成型语言模型 (LLM) 与人类情感词评级有很强的相关性,但往往会产生极端的分数,并表现出英语语言偏见. 法律法学可以帮助情感意义研究,并识别模型偏见.
科学领域:
- 计算语言学计算语言学
- 心理语言学 心理语言学
- 人工智能的人工智能
背景情况:
- 词语的情感内涵对于意义和社会行为至关重要.
- 生成式语言模型 (LLM) 可以复制人类的情感判断,并作为研究工具.
研究的目的:
- 评估三个LLM (GPT-4o,Mistral Large,Llama 3.1) 估计人类情感内涵等级的能力.
- 评估不同词类和语言文化 (英语,法语,德语) 的LLM绩效.
主要方法:
- LLM对社会身份,行为,修饰和设置术语的情感内涵进行了评价.
- 人类评分被收集用于英语 (美国),法语 (法国) 和德语 (德国) 的比较.
主要成果:
- 法学士评分与人类评分有很强的相关性,但往往更为极端.
- 意义维度的模式大致接近人类的评分.
- 在英语术语方面,LLM的表现更好,这表明了潜在的语言和文化偏见.
结论:
- 作为估计情感词语含义的工具,LLM表现有前途.
- 需要进一步的研究来解决LLM偏见,并完善他们的情感理解.
- 情感内涵奖学金可以指导LLM偏见的探索和发展.
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