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Objective quantification of mood states using large language models

Emotional states influence human behaviour and cognition, leading to diverse thought trajectories. Similarly, Large Language Models (LLMs) showcase an excellent level of response consistency across wide-ranging contexts (prompts). We leverage these parallels to establish a framework for quantifying mental states. Our approach utilises self-report questionnaires that reliably assess these states due to their inherent sensitivity to patterns of co-occurring responses. Specifically, we recruited a large sample of participants (N=422) to investigate how well an LLM (Mistral-7B-OpenOrca) quantifies a heterogenous set of depressive mood states measured with participants' open-ended responses to a depression questionnaire. We show LLM responses to held-out multiple-choice questions, given participants' open-ended answers, correlate strongly (r: 0.52-0.84) with true questionnaire scores, demonstrating LLM's generalisation from mood representations. We explore a link between these representations and factor analysis. Using ridge regression, we find depression-related subspaces within LLM hidden states. We show these subspaces to be predictive of participants' "Depression" and "Somatic & Emotional Distress" factor scores, as well as suicidality severity. Overall, LLMs can provide quantitative measures of mental states. The reliability of these hinges upon how informative the questions we ask participants are. Used correctly, this approach could supplement mental state assessment in a variety of settings.

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