Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918529033306112 |
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| author | Goel, Drishti Goyal, Agam Duddu, Veda Pal, Olivia Lee, Jeongah Zhong, Qiuyue Joy Rodriguez, Violeta J. Brown, Daniel S. Yoo, Dong Whi Karkar, Ravi Saha, Koustuv |
| author_facet | Goel, Drishti Goyal, Agam Duddu, Veda Pal, Olivia Lee, Jeongah Zhong, Qiuyue Joy Rodriguez, Violeta J. Brown, Daniel S. Yoo, Dong Whi Karkar, Ravi Saha, Koustuv |
| contents | Language models are increasingly being deployed for conversational support in informal caregiving contexts, where interactions often extend beyond information-seeking: caregivers seek emotional reassurance, guidance, and help, while navigating uncertain, relationally complex care decisions. Yet most safety evaluations assess model behavior under generic prompts, leaving a critical question unexamined: does a model's safety profile change with its support role? We study this by operationalizing four expert-reviewed support roles grounded in social support theory: Inform, Coach, Relate, and Listen, and comparing them against two baseline controls: a basic prompting condition and a retrieval-augmented generation (RAG) condition. We evaluate across three language models (GPT-4o-mini, Llama-3.1-8B-Instruct, and MedGemma-1.5-4b-it) on 5,000 real-world queries from online Alzheimer's Disease and Related Dementias (ADRD) communities. We find that the LLM's support role systematically shapes both the prevalence and composition of interactional risks. Furthermore, a human evaluation study reveals a perceived quality--safety tension: more directive, information-oriented roles are rated as more helpful and trustworthy despite exhibiting elevated interactional risk profiles. We release ~90,000 support role-conditioned model responses with risk annotations as an ecologically grounded resource for research on safer LLM-mediated conversational support. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_29473 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles Goel, Drishti Goyal, Agam Duddu, Veda Pal, Olivia Lee, Jeongah Zhong, Qiuyue Joy Rodriguez, Violeta J. Brown, Daniel S. Yoo, Dong Whi Karkar, Ravi Saha, Koustuv Human-Computer Interaction Artificial Intelligence Computation and Language Computers and Society Social and Information Networks Language models are increasingly being deployed for conversational support in informal caregiving contexts, where interactions often extend beyond information-seeking: caregivers seek emotional reassurance, guidance, and help, while navigating uncertain, relationally complex care decisions. Yet most safety evaluations assess model behavior under generic prompts, leaving a critical question unexamined: does a model's safety profile change with its support role? We study this by operationalizing four expert-reviewed support roles grounded in social support theory: Inform, Coach, Relate, and Listen, and comparing them against two baseline controls: a basic prompting condition and a retrieval-augmented generation (RAG) condition. We evaluate across three language models (GPT-4o-mini, Llama-3.1-8B-Instruct, and MedGemma-1.5-4b-it) on 5,000 real-world queries from online Alzheimer's Disease and Related Dementias (ADRD) communities. We find that the LLM's support role systematically shapes both the prevalence and composition of interactional risks. Furthermore, a human evaluation study reveals a perceived quality--safety tension: more directive, information-oriented roles are rated as more helpful and trustworthy despite exhibiting elevated interactional risk profiles. We release ~90,000 support role-conditioned model responses with risk annotations as an ecologically grounded resource for research on safer LLM-mediated conversational support. |
| title | Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2605.29473 |