大语言模型在情绪认知中的性别偏见
Gender Bias in Emotion Recognition by Large Language Models
AI总结：
本研究探讨了大语言模型在情绪认知任务中的性别偏见问题，并提出基于训练的干预方法以减少偏见。
摘要：
大语言模型（LLMs）的快速发展及其在日常生活中的日益深入整合，凸显了评估和确保其公平性的必要性。在本工作中，我们研究了情感理论中的公平性问题，探讨了当给出一个人及其环境的描述并被问及
英文摘要：
The rapid advancement of large language models (LLMs) and their growing integration into daily life underscore the importance of evaluating and ensuring their fairness. In this work, we examine fairness within the domain of emotional theory of mind, investigating whether LLMs exhibit gender biases when presented with a description of a person and their environment and asked, ''How does this person feel?''. Furthermore, we propose and evaluate several debiasing strategies, demonstrating that achieving meaningful reductions in bias requires training based interventions rather than relying solely on inference-time prompt-based approaches such as prompt engineering, etc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
