标题： 聊天增进依恋：使用大型语言模型预测成人关系 显示英文标题

标题： Chatting Up Attachment: Using LLMs to Predict Adult Bonds

摘要： 在医学领域获取数据具有挑战性，这使得该领域采用人工智能技术变得缓慢且高风险。 我们评估了是否可以使用大型语言模型（LLMs）生成的合成数据来克服这一障碍。 具体来说，我们使用GPT-4和Claude 3 Opus创建模拟具有不同档案、童年记忆和依恋风格的成年人的代理。 这些代理参与模拟成人依恋访谈（AAI），我们使用它们的回答来训练预测其潜在依恋风格的模型。 我们使用由9个人类完成的相同访谈协议的转录数据集（由心理健康专业人士分析和标注）来评估我们的模型。 我们的研究结果表明，仅使用合成数据训练模型的表现与使用人类数据训练模型的表现相当。 此外，虽然合成答案的原始嵌入与真实人类回答的嵌入占据不同的空间，但引入未标记的人类数据和简单的标准化允许这些表示更紧密地对齐。 这种调整得到了定性分析的支持，并反映在标准化嵌入的增强预测准确性上。 显示英文摘要

摘要： Obtaining data in the medical field is challenging, making the adoption of AI technology within the space slow and high-risk. We evaluate whether we can overcome this obstacle with synthetic data generated by large language models (LLMs). In particular, we use GPT-4 and Claude 3 Opus to create agents that simulate adults with varying profiles, childhood memories, and attachment styles. These agents participate in simulated Adult Attachment Interviews (AAI), and we use their responses to train models for predicting their underlying attachment styles. We evaluate our models using a transcript dataset from 9 humans who underwent the same interview protocol, analyzed and labeled by mental health professionals. Our findings indicate that training the models using only synthetic data achieves performance comparable to training the models on human data. Additionally, while the raw embeddings from synthetic answers occupy a distinct space compared to those from real human responses, the introduction of unlabeled human data and a simple standardization allows for a closer alignment of these representations. This adjustment is supported by qualitative analyses and is reflected in the enhanced predictive accuracy of the standardized embeddings.