用大模型分析口语，零样本预测心理幸福感，最高相关性达0.8。
Predicting Psychological Well-Being from Spontaneous Speech using LLMs
- 用临床专家设计的提示词，让大模型从自发口语中提取心理状态线索。
- 在111人数据上，模型对心理幸福感的预测相关性最高达0.8，覆盖80%数据。
- 通过词云和统计分析揭示关键语言特征，提升预测可解释性，适合心理健康研究者。
我们研究了大语言模型（LLMs）在零样本条件下，从自发语音中预测瑞夫心理幸福感（PWB）评分的可行性。基于PsyVoiD数据库中111名参与者的数分钟语音记录，评估了12个指令微调的LLM，包括Llama-3（8B、70B）、Ministral、Mistral、Gemma-2-9B、Gemma-3（1B、4B、27B）、Phi-4、DeepSeek（Qwen和Llama）以及QwQ-Preview。结合临床心理学与语言学专家开发了领域知情提示词。结果表明，大模型能从自发口语中提取语义上有意义的线索，在80%的数据上实现高达0.8的斯皮尔曼相关性。为增强可解释性，我们进行了统计分析以刻画预测变异性与系统性偏差，并通过关键词词云分析识别出驱动模型预测的语言特征。
原文摘要 · Abstract (English)
We investigate the use of Large Language Models (LLMs) for zero-shot prediction of Ryff Psychological Well-Being (PWB) scores from spontaneous speech. Using a few minutes of voice recordings from 111 participants in the PsyVoiD database, we evaluated 12 instruction-tuned LLMs, including Llama-3 (8B, 70B), Ministral, Mistral, Gemma-2-9B, Gemma-3 (1B, 4B, 27B), Phi-4, DeepSeek (Qwen and Llama), and QwQ-Preview. A domain-informed prompt was developed in collaboration with experts in clinical psychology and linguistics. Results show that LLMs can extract semantically meaningful cues from spontaneous speech, achieving Spearman correlations of up to 0.8 on 80\% of the data. Additionally, to enhance explainability, we conducted statistical analyses to characterise prediction variability and systematic biases, alongside keyword-based word cloud analyses to highlight the linguistic features driving the models' predictions.
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。