Investigating the Influence of Language on Sycophantic Behavior of Multilingual LLMs
Large language models (LLMs) have achieved strong performance across a wide range of tasks, but they are also prone to sycophancy, the tendency to agree with user statements regardless of validity. Previous research has outlined both the extent and the underlying causes of sycophancy in earlier models, such as ChatGPT-3.5 and Davinci. Newer models have since undergone multiple mitigation strategies, yet there remains a critical need to systematically test their behavior. In particular, the effect of language on sycophancy has not been explored. In this work, we investigate how the language influences sycophantic responses. We evaluate three state-of-the-art models, GPT-4o mini, Gemini 1.5...
| Authors | Bayan Abdullah Aldahlawi, A. B. M. Ashikur Rahman, Irfan Ahmad |
|---|---|
| Primary category | cs.CL |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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