When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901855042273280 |
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| author | Kolb, Christian |
| author_facet | Kolb, Christian |
| contents | Large language models (LLMs) are increasingly used in clinical and care settings. This exploratory study investigates whether LLMs exhibit sycophantic behavior — adapting their responses to social expectation signals rather than maintaining professional quality — in the context of dementia care. Five prompts with systematically increasing confirmatory and authority-related framing (P1 neutral to P5 authority-signaled implementation support) were submitted to four LLMs (GPT-5, Claude Sonnet 4.6, Gemini 3.1 Pro, Mistral Large), each repeated five times (N = 100 responses). Responses were evaluated using an LLM-as-a-Judge methodology against seven nursing-ethical quality criteria (K1–K7) and a tone scale (0–3). All models showed significant negative Spearman correlations between prompt level and response quality (ρ ranging from −0.543 to −0.734, all p < 0.01). The findings suggest that LLMs pose context-sensitive risks in high-stakes care environments and that prompt framing significantly shapes response quality. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19548622 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings Kolb, Christian sycophancy large language models dementia care nursing LLM evaluation patient safety LLM-as-a-Judge prompt engineering Large language models (LLMs) are increasingly used in clinical and care settings. This exploratory study investigates whether LLMs exhibit sycophantic behavior — adapting their responses to social expectation signals rather than maintaining professional quality — in the context of dementia care. Five prompts with systematically increasing confirmatory and authority-related framing (P1 neutral to P5 authority-signaled implementation support) were submitted to four LLMs (GPT-5, Claude Sonnet 4.6, Gemini 3.1 Pro, Mistral Large), each repeated five times (N = 100 responses). Responses were evaluated using an LLM-as-a-Judge methodology against seven nursing-ethical quality criteria (K1–K7) and a tone scale (0–3). All models showed significant negative Spearman correlations between prompt level and response quality (ρ ranging from −0.543 to −0.734, all p < 0.01). The findings suggest that LLMs pose context-sensitive risks in high-stakes care environments and that prompt framing significantly shapes response quality. |
| title | When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings |
| topic | sycophancy large language models dementia care nursing LLM evaluation patient safety LLM-as-a-Judge prompt engineering |
| url | https://doi.org/10.5281/zenodo.19548622 |