A Human-Centred Approach to Benchmarking LLMs for Parenting Advice

Abstract:People are increasingly using large language models (LLMs) to seek advice, including for parenting. Parenting is a critical and socially sensitive domain. Thus, evaluating advice provided by LLMs requires indicators beyond aggregated information quality benchmarks to consider relational and behavioural elements of the responses. With a multi-dimensional rubric created by parenting experts, this paper evaluates 15 LLMs across 100 parenting scenarios in 2 languages (English and Chinese), using an LLM-as-a-judge method. Results show that aggregate scores can hide rubric item-specific weaknesses, models implicitly encourage different parenting styles, and language influences responses. We highlight the importance of evaluation output auditability and challenges involved in evaluating LLM-generated advice in domains like parenting. Our findings provide important insights for selecting LLMs for direct user engagement and the development of user-facing parenting advice applications.

15 pages. Submitted for review

Artificial Intelligence (cs.AI)

arXiv:2608.14622 [cs.AI]

(or arXiv:2608.14622v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.14622 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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