ELEPHANT: Measuring and understanding social sycophancy in LLMs
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917381109972992 |
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| author | Cheng, Myra Yu, Sunny Lee, Cinoo Khadpe, Pranav Ibrahim, Lujain Jurafsky, Dan |
| author_facet | Cheng, Myra Yu, Sunny Lee, Cinoo Khadpe, Pranav Ibrahim, Lujain Jurafsky, Dan |
| contents | LLMs are known to exhibit sycophancy: agreeing with and flattering users, even at the cost of correctness. Prior work measures sycophancy only as direct agreement with users' explicitly stated beliefs that can be compared to a ground truth. This fails to capture broader forms of sycophancy such as affirming a user's self-image or other implicit beliefs. To address this gap, we introduce social sycophancy, characterizing sycophancy as excessive preservation of a user's face (their desired self-image), and present ELEPHANT, a benchmark for measuring social sycophancy in an LLM. Applying our benchmark to 11 models, we show that LLMs consistently exhibit high rates of social sycophancy: on average, they preserve user's face 45 percentage points more than humans in general advice queries and in queries describing clear user wrongdoing (from Reddit's r/AmITheAsshole). Furthermore, when prompted with perspectives from either side of a moral conflict, LLMs affirm both sides (depending on whichever side the user adopts) in 48% of cases--telling both the at-fault party and the wronged party that they are not wrong--rather than adhering to a consistent moral or value judgment. We further show that social sycophancy is rewarded in preference datasets, and that while existing mitigation strategies for sycophancy are limited in effectiveness, model-based steering shows promise for mitigating these behaviors. Our work provides theoretical grounding and an empirical benchmark for understanding and addressing sycophancy in the open-ended contexts that characterize the vast majority of LLM use cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ELEPHANT: Measuring and understanding social sycophancy in LLMs Cheng, Myra Yu, Sunny Lee, Cinoo Khadpe, Pranav Ibrahim, Lujain Jurafsky, Dan Computation and Language Artificial Intelligence Computers and Society LLMs are known to exhibit sycophancy: agreeing with and flattering users, even at the cost of correctness. Prior work measures sycophancy only as direct agreement with users' explicitly stated beliefs that can be compared to a ground truth. This fails to capture broader forms of sycophancy such as affirming a user's self-image or other implicit beliefs. To address this gap, we introduce social sycophancy, characterizing sycophancy as excessive preservation of a user's face (their desired self-image), and present ELEPHANT, a benchmark for measuring social sycophancy in an LLM. Applying our benchmark to 11 models, we show that LLMs consistently exhibit high rates of social sycophancy: on average, they preserve user's face 45 percentage points more than humans in general advice queries and in queries describing clear user wrongdoing (from Reddit's r/AmITheAsshole). Furthermore, when prompted with perspectives from either side of a moral conflict, LLMs affirm both sides (depending on whichever side the user adopts) in 48% of cases--telling both the at-fault party and the wronged party that they are not wrong--rather than adhering to a consistent moral or value judgment. We further show that social sycophancy is rewarded in preference datasets, and that while existing mitigation strategies for sycophancy are limited in effectiveness, model-based steering shows promise for mitigating these behaviors. Our work provides theoretical grounding and an empirical benchmark for understanding and addressing sycophancy in the open-ended contexts that characterize the vast majority of LLM use cases. |
| title | ELEPHANT: Measuring and understanding social sycophancy in LLMs |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2505.13995 |