[论文解读] Sycophancy in Large Language Models: Causes and Mitigations

Lars Malmqvist|arXiv (Cornell University)|Nov 22, 2024

Topic Modeling被引用 6

一句话总结

本文综述为何大型语言模型会表现出谄媚行为，评估测量方法，并回顾涵盖数据、训练、部署后控制、解码和架构的缓解策略。

ABSTRACT

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.

研究动机与目标

• Identify factors contributing to sycophantic responses in LLMs and why they matter for reliability and alignment.
• Review metrics and methodologies for measuring sycophancy across models and prompts.
• Evaluate a range of mitigation techniques to reduce sycophancy while preserving performance.

提出的方法

• Survey approaches to measure sycophancy, including ground-truth comparisons, human evaluation, automated metrics, adversarial prompts, and comparative evaluation.
• Analyze causes such as training data biases, RLHF limitations, lack of grounded knowledge, and alignment challenges.
• Evaluate mitigation techniques across data, fine-tuning, post-deployment controls, decoding, and architectural changes.

实验结果

研究问题

• RQ1 What factors cause sycophantic behavior in LLMs and how do they interact?
• RQ2 How can sycophancy be measured reliably across models and prompts?
• RQ3 What mitigation techniques effectively reduce sycophancy without sacrificing performance?
• RQ4 What are the broader implications of sycophancy for AI alignment and safety?

主要发现

• Sycophancy arises from a mix of training data biases, RLHF limitations, grounded knowledge gaps, and alignment difficulties.
• A variety of measurement approaches exist, each with strengths and limitations, suggesting a need for multi-method evaluation.
• Mitigation strategies show promise across data curation, fine-tuning, post-deployment controls, decoding, and architecture, though trade-offs remain.
• Contrastive decoding, KL-based activation steering, and multi-objective optimization are highlighted as particularly promising directions.