Ask the experts: Sourcing a high-quality nutrition counseling dataset through Human-AI collaboration

Large Language Models (LLMs) are being employed by end-users for various tasks, including sensitive ones such as health counseling, disregarding potential safety concerns. It is thus necessary to understand how adequately LLMs perform in such domains. We conduct a case study on ChatGPT in nutrition counseling, a popular use-case where the model supports a user with their dietary struggles. We crowdsource real-world diet-related struggles, then work with nutrition experts to generate supportive text using ChatGPT. Finally, experts evaluate the safety and text quality of ChatGPT's output. The result is the HAI-Coaching dataset, containing ∼2.4K crowdsourced dietary struggles and ∼97K corresponding ChatGPT-generated and expert-annotated supportive texts. We analyse ChatGPT's performance, discovering potentially harmful behaviours, especially for sensitive topics like mental health. Finally, we use HAI-Coaching to test open LLMs on various downstream tasks, showing that even the latest models struggle to achieve good performance. HAI-Coaching is available at https://github.com/uccollab/hai-coaching/.