Ask the experts: sourcing high-quality datasets for nutritional counselling through Human-AI collaboration

TL;DR

This paper demonstrates a human-AI collaborative approach to create a high-quality nutrition counselling dataset by combining crowd-sourcing, expert input, and ChatGPT, highlighting both its potential and risks.

Contribution

It introduces HAI-coaching, the first expert-annotated nutrition dataset, and presents a novel methodology for dataset creation in low-resource domains using LLMs and human expertise.

Findings

ChatGPT generates fluent, human-like supportive texts.

Generated texts can contain harmful biases, especially in sensitive topics.

The dataset enables future research in nutrition counselling AI applications.

Abstract

Large Language Models (LLMs), with their flexible generation abilities, can be powerful data sources in domains with few or no available corpora. However, problems like hallucinations and biases limit such applications. In this case study, we pick nutrition counselling, a domain lacking any public resource, and show that high-quality datasets can be gathered by combining LLMs, crowd-workers and nutrition experts. We first crowd-source and cluster a novel dataset of diet-related issues, then work with experts to prompt ChatGPT into producing related supportive text. Finally, we let the experts evaluate the safety of the generated text. We release HAI-coaching, the first expert-annotated nutrition counselling dataset containing ~2.4K dietary struggles from crowd workers, and ~97K related supportive texts generated by ChatGPT. Extensive analysis shows that ChatGPT while producing highly fluent and human-like text, also manifests harmful behaviours, especially in sensitive topics like mental health, making it unsuitable for unsupervised use.