大模型能预测不同领域态度间的关联，即使表面不相似。
Can Large Language Models Predict Associations Among Human Attitudes?
- 用新数据集测试GPT-4o在跨领域态度间建模相关性。
- 模型在无表面相似性时仍能准确预测态度关联。
- 适合研究社会认知、信念结构的学者参考。
先前研究显示大语言模型（LLMs）可基于相似态度预测人类态度，但多限于高度相关领域。而人类态度常在看似无关的主题间存在强关联。我们构建了一个涵盖多样态度陈述的人类响应新数据集，发现前沿模型GPT-4o不仅能复现个体态度间的成对相关性，还能从一个态度预测另一个。关键突破在于：我们检验了当态度间无表面相似性时的预测能力，结果表明虽表面相似性提升准确率，但模型仍能有效生成跨领域社会推断。整体表明，大语言模型捕捉到了人类信念系统中深层的潜在结构。
原文摘要 · Abstract (English)
Prior work has shown that large language models (LLMs) can predict human attitudes based on other attitudes, but this work has largely focused on predictions from highly similar and interrelated attitudes. In contrast, human attitudes are often strongly associated even across disparate and dissimilar topics. Using a novel dataset of human responses toward diverse attitude statements, we found that a frontier language model (GPT-4o) was able to recreate the pairwise correlations among individual attitudes and to predict individuals' attitudes from one another. Crucially, in an advance over prior work, we tested GPT-4o's ability to predict in the absence of surface-similarity between attitudes, finding that while surface similarity improves prediction accuracy, the model was still highly-capable of generating meaningful social inferences between dissimilar attitudes. Altogether, our findings indicate that LLMs capture crucial aspects of the deeper, latent structure of human belief systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。