在使用LLM作为人体代孕时要小心
Yuan Gao1, Dokyun Lee1,2, Gordon Burtch1
1Department of Information Systems, Questrom School of Business, Boston University, Boston, MA 02215.
概括
大型语言模型 (LLM) 是有前途的,但在社会科学研究中难以复制人类行为. 它们的概率性质,与人类认知不同,限制了它们作为人类参与者的可靠替代品的使用.
科学领域:
- 人工智能的人工智能
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
背景情况:
- 大型语言模型 (LLM) 展示了类似人类的文本生成能力.
- 在社会科学研究中,LLM越来越多地被提议作为人类参与者的替代品.
- 在LLM处理和人类认知之间存在根本的差异,包括体现的经验和生存驱动器.
研究的目的:
- 评估LLM在社会科学研究中模拟人类行为的能力.
- 评估LLM在经济决策任务中的推理深度.
- 在使用LLM作为人类替代品时识别限制和潜在偏见.
主要方法:
- 利用11-20金钱请求游戏来测试LLM的推理.
- 与已建立的人类行为分布对比LLM响应.
- 分析了各种先进的LLM方法及其在不同模型中的表现.
主要成果:
- 大多数先进的LLM方法都无法准确地复制人类行为分布.
- 故障模式是多样化和不可预测的,受输入语言和模型保护等因素的影响.
- 在LLM输出和人类决策模式之间观察到显著的差异.
结论:
- 尽管LLM的反应与人类相似,但由于基本的认知差异,LLM不能可靠地模拟人类行为.
- 建议在使用LLM作为替代品或在研究中模拟人类行为时谨慎使用.
- 需要进一步的研究来理解和减轻社会科学应用中的LLM限制.
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