{"slug": "keyphrase-generative-representation-of-youth-crisis-conversations-beyond-static", "title": "Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies", "summary": "Researchers analyzed 703,975 de-identified Kids Help Phone conversations from 2018-2023 and expanded the platform's 19-label issue taxonomy into a 39-label hierarchical schema. They introduced Keyphrase Generative Representation (KGR), a constrained large language model that generated conversation-specific keyphrases, achieving 81% accuracy in reflecting content and surfacing identity-linked themes such as immigration problems and caregiver burden. The KGR-supported topic-retrieval workflow increased accuracy from 0.25 to 0.70 over manual analysis, marking a shift toward hybrid, interpretable representations that capture emerging and culturally grounded patterns of youth distress beyond static taxonomies.", "body_md": "arXiv:2605.27546v1 Announce Type: new\nAbstract: Crisis Responders (CRs) rapidly assess thousands of youth SMS conversations each year to identify mental health concerns and guide support. Yet youth distress is increasingly expressed through evolving and context-specific language that often does not fit fixed-label taxonomies. This work analyzed 703,975 de-identified Kids Help Phone conversations (2018-2023) and expanded KHP's 19-label issue taxonomy into a 39-label hierarchical schema. We then introduce Keyphrase Generative Representation (KGR), a constrained LLM generating concise, conversation-specific keyphrases, evaluated across 129 conversations and 387 expert annotations. The expanded taxonomy achieved expert consensus reliability, with an accuracy of 0.96, and expert review found that 81% of keyphrases accurately reflected content and 74% improved clarity. KGR surfaced identity-linked themes absent from the fixed taxonomy, including immigration problems and caregiver burden, and supported a topic-retrieval workflow that increased accuracy from 0.25 to 0.70 (+0.45) over the manual analyst process. KGR marks a shift toward hybrid, interpretable generative representations that extend crisis response beyond static taxonomies to surface emerging and culturally grounded patterns of youth distress.", "url": "https://wpnews.pro/news/keyphrase-generative-representation-of-youth-crisis-conversations-beyond-static", "canonical_source": "https://arxiv.org/abs/2605.27546", "published_at": "2026-05-28 04:00:00+00:00", "updated_at": "2026-05-28 04:35:07.912918+00:00", "lang": "en", "topics": ["large-language-models", "natural-language-processing", "generative-ai", "ai-research", "ai-ethics"], "entities": ["Kids Help Phone", "KHP"], "alternates": {"html": "https://wpnews.pro/news/keyphrase-generative-representation-of-youth-crisis-conversations-beyond-static", "markdown": "https://wpnews.pro/news/keyphrase-generative-representation-of-youth-crisis-conversations-beyond-static.md", "text": "https://wpnews.pro/news/keyphrase-generative-representation-of-youth-crisis-conversations-beyond-static.txt", "jsonld": "https://wpnews.pro/news/keyphrase-generative-representation-of-youth-crisis-conversations-beyond-static.jsonld"}}