Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles

Abstract: LLMs 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 LLMs (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.

• The paper demonstrates that the role assigned to a language model in caregiving contexts significantly impacts the prevalence and composition of interactional risks, even with identical queries and retrieved evidence (e.g., Kruskal-Wallis H up to 91.5 across [RubRIX risk] dimensions).
• Different roles (Inform, Coach, Relate, Listen) induce consistent shifts in communicative style, with Coach and Inform increasing cognitive processing language and Listen and Relate increasing pronoun-oriented language (e.g., flexibility shifts noted in coach, and listen).
• A significant misalignment exists between what users perceive as helpful and what is assessed as risky by clinicians. Although coaches and informers are often deemed helpful and actionable (e.g., overall 3.81 & 3.79), they exhibit higher risks based on the rubric entries assessed.

Study overview

This paper investigates whether the communicative support role assigned to a LLM changes its interactional risk profile and perceived response quality in caregiving contexts (2605.29473). The authors operationalize four theory-grounded, clinician-validated roles—Inform (explanatory information), Coach (directive action planning), Relate (emotional validation and peer-like normalization), and Listen (non-directive reflective listening)—drawn from social support theory, alongside two controls: Default (no role scaffold) and Retrieval-only (evidence summarization without role framing). The domain is Alzheimer's Disease and Related Dementias (ADRD) caregiving, chosen because caregiver support needs are rarely purely informational and are entangled with distress, guilt, and ambivalence.

The central empirical question is whether safety evaluations that hold prompting constant miss a deployment-relevant variable. The paper's answer is affirmative: for identical queries and retrieved evidence, changing only the role produces statistically significant differences in both the prevalence and composition of clinician-informed interactional risks—and, notably, these risks diverge from what human evaluators rate as helpful or trustworthy.

Data and experimental design

The study uses 5,000 real-world caregiver queries collected from Reddit r/Alzheimers and ALZConnected, filtered by length and public engagement. Queries are labeled along two binary dimensions—informational support need (IS) and emotional support need (ES)—using an LLM-as-judge procedure validated against human annotations ($\kappa = 0.80$ for IS, $0.92$ for ES; macro-F1 of 0.782/0.701 versus 0.652/0.631 for a prior framework). Most queries express combined needs (IS=1, ES=1: 42.0%), followed by informational-only (35.5%) and emotional-only (17.3%).

Responses are generated by three models spanning proprietary, open-source, and medically fine-tuned families—GPT-4o-mini, Llama-3.1-8B-Instruct, and MedGemma-1.5-4b-it—under six conditions per query, conditioned on retrieved evidence from a RAG pipeline over PubMedQA, MedQuAD, and ADRD-specific web pages (3,872 passages embedded via SentenceBERT into a FAISS index; top-3 retrieval). This yields approximately 90,000 role-conditioned responses, released with risk annotations.

Risk is measured with RubRIX, a clinician-informed rubric covering five dimensions: Inattention, Bias & Stigma, Information Inaccuracy, Uncritical Affirmation, and Epistemic Arrogance, scored via GPT-5-nano as judge. Human evaluation involved 125 Prolific participants rating 1,113 query–response pairs on seven quality dimensions derived from the Social Support Behavior Code.

Role effects on interactional risk (RQ1)

Support-role differences in aggregate RubRIX risk are significant in 10 of 12 model–support-need strata ($p < 0.001$ in all significant cases), with non-significant cells occurring only for GPT-4o-mini under no-support-need and emotional-only strata. However, directionality is strongly model-dependent:

Model	Pattern relative to controls
GPT-4o-mini	Modest effects; Coach (.103) and Listen (.095) raise risk vs. Default (.055) in informational-only queries
Llama-3.1-8B	Roles consistently lower risk than both controls (e.g., .074–.144 vs. .305 Default in informational-only)
MedGemma	Highest sensitivity (Kruskal-Wallis H up to 91.5); Inform and Relate lower risk; Coach raises it substantially

This heterogeneity is an important caveat: the paper does not claim a universal ordering of roles by risk. Rather, the robust finding is that role assignment matters, not which specific role is safest across all models.

At the dimension level, Kruskal-Wallis tests show significant distributional differences across all five RubRIX dimensions when controls are included (H-statistics ranging from 13.3 to 700.0), and in 14 of 15 dimension–model cells restricted to the four roles alone. Role assignment therefore reshapes not just total risk but its composition—for instance, Information Inaccuracy shows the largest differentiation (H up to 700.0 for Llama).

Linguistic analyses reinforce this picture. All roles shorten responses relative to baselines (strongest for Listen: Cohen's $d$ of −2.12 to −3.73), while length-adjusted regressions confirm that Inform and Coach remain syntactically denser and more complex even after controlling for word count, and Relate remains less formal. LIWC analyses reveal interpretable stance shifts: Relate and Listen increase pronoun-oriented language, Inform increases cognitive-processing language, and Coach suppresses affective and embodied language. Formal role-by-model interaction tests confirm these effects vary in magnitude across model families for all linguistic features tested.

Perceived quality and the helpfulness–safety tension (RQ2)

Human evaluators rate roles differently on every dimension ($p < .001$), with a clear overall gradient: Coach (3.81) > Inform (3.79) > Relate (3.59) > Listen (3.10). Coach and Inform dominate on Helpful/Actionable and Provides Useful Information; Relate leads on Understands Emotions (3.96) and Provides Emotional Support (3.83). Listen scores lowest on all seven dimensions, with pairwise contrasts against every other role significant on most dimensions (rank-biserial correlations up to 0.62 for Provides Info, Inform vs. Listen).

The central and arguably most consequential finding is the divergence between perceived quality and clinician-informed risk: Coach and Inform receive comparatively high ratings on helpfulness, actionability, and trustworthiness despite exhibiting elevated RubRIX risk profiles, particularly for GPT-4o-mini and MedGemma. The authors frame this as a potential tension in high-stakes deployments—the response styles users find most competent may simultaneously carry more interactional risk, and sustained exposure could increase reliance on incomplete or overconfident guidance.

The Listen result carries a distinct implication. Reflective listening is well validated in clinician-centered counseling, yet it is rated worst when enacted by an LLM, suggesting that communicative practices effective in human therapeutic encounters may not transfer straightforwardly to AI-mediated support, where users expect concrete guidance. The authors caution that users who perceive constrained, hedging systems as unhelpful may migrate toward less-safeguarded systems offering direct, confident advice—a downstream adoption dynamic the single-turn design cannot test directly.

Limitations and open questions

The authors are explicit about several constraints. The human evaluation used general Prolific participants rather than actual ADRD caregivers, whose judgments may differ; recruiting caregivers at scale is left as future work given ethical considerations around vulnerable populations. Risk scoring relies on LLM-as-judge (GPT-5-nano), which cannot fully substitute for clinician adjudication despite RubRIX's validation history. The study is single-turn, so longitudinal risk dynamics such as accumulated reliance on overconfident guidance remain unmeasured, and no downstream outcomes (caregiver wellbeing, adherence, care decisions) are established. Findings may not generalize beyond the three models evaluated, and results depend on one specific protocol instantiation per role—alternative operationalizations could produce different profiles. The dataset also skews toward English-language, digitally engaged populations. Finally, the authors explicitly disclaim any claim that particular roles are clinically superior interventions.

Conclusion

By holding queries and retrieved evidence constant while varying only the enacted support role, this work demonstrates that role configuration functions as a deployment-time safety variable rather than a stylistic choice. Across three models and roughly 90,000 responses, role assignment systematically alters both the prevalence and composition of interactional risks, induces stable, length-independent shifts in communicative style, and produces a measurable misalignment between what humans rate as helpful and what clinician-informed rubrics flag as risky. The released corpus of role-conditioned, risk-annotated responses provides a resource for evaluating safer conversational support systems, though establishing whether these interactional differences translate into real-world caregiver outcomes remains unresolved.