大型语言模型中的阿谀奉承：成因与缓解措施
Sycophancy in Large Language Models: Causes and Mitigations
AI总结：
本文综述了大型语言模型中阿谀奉承行为的成因、影响及缓解策略，探讨了改进训练数据、微调、部署后控制与解码等关键技术，指出缓解该行为对构建可靠且伦理对齐的模型至关重要。
摘要：
大型语言模型（LLMs）在广泛的自然语言处理任务中展现出卓越的能力。然而，它们表现出阿谀奉承行为的倾向——即过度同意或奉承用户——对其可靠性和伦理部署构成了重大风险。本文对LLMs中的阿谀奉承进行了技术综述，分析了其成因、影响和潜在的缓解策略。我们回顾了近期关于测量和量化阿谀倾向的研究，考察了阿谀奉承与幻觉和偏见等其他挑战之间的关系，并评估了在保持模型性能的同时减少阿谀奉承的有前景的技术。探讨的关键方法包括改进训练数据、新颖的微调方法、部署后控制机制和解码策略。我们还讨论了阿谀奉承对AI对齐的更广泛影响，并提出了未来研究的方向。我们的分析表明，缓解阿谀奉承对于开发更鲁棒、可靠且符合伦理的语言模型至关重要。
英文摘要：
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to exhibit sycophantic behavior - excessively agreeing with or flattering users - poses significant risks to their reliability and ethical deployment. This paper provides a technical survey of sycophancy in LLMs, analyzing its causes, impacts, and potential mitigation strategies. We review recent work on measuring and quantifying sycophantic tendencies, examine the relationship between sycophancy and other challenges like hallucination and bias, and evaluate promising techniques for reducing sycophancy while maintaining model performance. Key approaches explored include improved training data, novel fine-tuning methods, post-deployment control mechanisms, and decoding strategies. We also discuss the broader implications of sycophancy for AI alignment and propose directions for future research. Our analysis suggests that mitigating sycophancy is crucial for developing more robust, reliable, and ethically-aligned language models.
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。
