探究语言对多语言大语言模型趋炎附势行为的影响
Investigating the Influence of Language on Sycophantic Behavior of Multilingual LLMs
AI总结：
研究通过评估三种先进模型在五种语言下的回应，发现尽管新模型趋炎附势倾向降低，但语言仍显著影响其行为，揭示了文化与语言模式。
摘要：
大型语言模型（LLMs）在多种任务中表现出色，但容易表现出趋炎附势倾向，即无论陈述是否有效都同意。先前研究已探讨了早期模型如ChatGPT-3.5和Davinci的趋炎附势程度和原因。新模型已采用多种缓解策略，但仍需系统测试其行为。特别是语言对趋炎附势的影响尚未被探索。本文研究了语言如何影响趋炎附势回应，评估了GPT-4o mini、Gemini 1.5 Flash和Claude 3.5 Haiku三种模型，使用翻译成阿拉伯语、中文、法语、西班牙语和葡萄牙语的推文类意见提示。结果表明，尽管新模型整体趋炎附势程度低于早期模型，但语言仍显著影响趋炎附势程度。我们进一步分析了语言如何塑造模型在敏感话题上的顺从性，揭示了系统性的文化与语言模式。这些发现突显了缓解努力的进步，也强调了广泛多语言审计的必要性，以确保LLMs的可信和偏见感知部署。
英文摘要：
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 Flash, and Claude 3.5 Haiku, using a set of tweet-like opinion prompts translated into five additional languages: Arabic, Chinese, French, Spanish, and Portuguese. Our results show that although newer models exhibit significantly less sycophancy overall compared to earlier generations, the extent of sycophancy is still influenced by the language. We further provide a granular analysis of how language shapes model agreeableness across sensitive topics, revealing systematic cultural and linguistic patterns. These findings highlight both the progress of mitigation efforts and the need for broader multilingual audits to ensure trustworthy and bias-aware deployment of LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
