构建首个面向心理健康的大语言模型函数调用数据集。
mind_call: A Dataset for Mental Health Function Calling with Large Language Models
- 基于可穿戴设备健康信号生成自然语言到API的映射。
- 涵盖显式、隐喻等五类真实心理交互表达。
- 适合研究心理辅助智能体与函数调用可靠性。
基于大语言模型的系统越来越多地依赖函数调用以实现与外部数据源的结构化和可控交互，但现有数据集未涵盖面向心理健康、访问可穿戴传感器数据的应用。本文提出一个合成的函数调用数据集，用于支持心理健康辅助，其依据睡眠、身体活动、心血管指标、压力信号和代谢数据等可穿戴健康信号构建。数据集将多样化的自然语言查询映射到标准化API调用，每个样本包含用户查询、查询类别、明确推理步骤、归一化时间参数和目标函数。数据覆盖显式、隐式、行为、症状及隐喻性表达，反映真实心理相关交互。该资源支持意图定位、时间推理与可靠函数调用的研究，已公开发布以促进可复现性与后续工作。
原文摘要 · Abstract (English)
Large Language Model (LLM)-based systems increasingly rely on function calling to enable structured and controllable interaction with external data sources, yet existing datasets do not address mental health-oriented access to wearable sensor data. This paper presents a synthetic function-calling dataset designed for mental health assistance grounded in wearable health signals such as sleep, physical activity, cardiovascular measures, stress indicators, and metabolic data. The dataset maps diverse natural language queries to standardized API calls derived from a widely adopted health data schema. Each sample includes a user query, a query category, an explicit reasoning step, a normalized temporal parameter, and a target function. The dataset covers explicit, implicit, behavioral, symptom-based, and metaphorical expressions, which reflect realistic mental health-related user interactions. This resource supports research on intent grounding, temporal reasoning, and reliable function invocation in LLM-based mental health agents and is publicly released to promote reproducibility and future work.
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