A Human-Centred Approach to Benchmarking LLMs for Parenting Advice

[2608.14622] A Human-Centred Approach to Benchmarking LLMs for Parenting Advice Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14622 (cs) [Submitted on 14 Jul 2026] Title: A Human-Centred Approach to Benchmarking LLMs for Parenting Advice Authors: Yunke Zhao , Isobel Voysey , Alastair van Heerden , Rob Hughes , Jun Zhao View a PDF of the paper titled A Human-Centred Approach to Benchmarking LLMs for Parenting Advice, by Yunke Zhao and Isobel Voysey and Alastair van Heerden and Rob Hughes and Jun Zhao View PDF HTML (experimental) 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. Comments: 15 pages. Submitted for review Subjects: Artificial Intelligence (cs.AI) Cite as: 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 Submission history From: Jun Zhao Dr [ view email ] [v1] Tue, 14 Jul 2026 09:27:16 UTC (4,016 KB) Full-text links: Access Paper: View a PDF of the paper titled A Human-Centred Approach to Benchmarking LLMs for Parenting Advice, by Yunke Zhao and Isobel Voysey and Alastair van Heerden and Rob Hughes and Jun Zhao View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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