Karen Jia-Hui Li
2024
Ask the experts: sourcing a high-quality nutrition counseling dataset through Human-AI collaboration
Simone Balloccu
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Ehud Reiter
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Karen Jia-Hui Li
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Rafael Sargsyan
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Vivek Kumar
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Diego Reforgiato
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Daniele Riboni
|
Ondrej Dusek
Findings of the Association for Computational Linguistics: EMNLP 2024
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 crowd-source 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/
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Co-authors
- Simone Balloccu 1
- Ehud Reiter 1
- Rafael Sargsyan 1
- Vivek Kumar 1
- Diego Reforgiato 1
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