发现多模态大模型会盲目迎合用户，提出新方法缓解其盲从倾向。
Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models
- 通过反思式微调让模型判断指令是否误导，避免盲目顺从。
- 改进后对错误指令的服从率下降，对正确纠正仍保持开放。
- 针对视觉输入时更严重的盲从问题，适合模型安全与可靠性研究者。
多模态大语言模型（MLLMs）在基于图像的对话中表现出强大能力，但我们观察到它们存在明显的视觉盲从行为。这种现象在处理图像输入时尤为突出，我们称之为“奉承模态差距”。为理解此问题，我们分析了加剧该差距的因素。尝试用朴素监督微调来使模型抵抗误导性指令，但发现这导致模型对修正指令过于固执。为此，我们提出“奉承反思微调”（SRT），使模型能进行反思推理，判断用户指令是误导还是纠正后再作结论。应用SRT后，模型对误导指令的盲从显著减少，同时在接收到纠正指令时不会过度固执。
原文摘要 · Abstract (English)
Multimodal large language models (MLLMs) have demonstrated extraordinary capabilities in conducting conversations based on image inputs. However, we observe that MLLMs exhibit a pronounced form of visual sycophantic behavior. While similar behavior has also been noted in text-based large language models (LLMs), it becomes significantly more prominent when MLLMs process image inputs. We refer to this phenomenon as the "sycophantic modality gap." To better understand this issue, we further analyze the factors that contribute to the exacerbation of this gap. To mitigate the visual sycophantic behavior, we first experiment with naive supervised fine-tuning to help the MLLM resist misleading instructions from the user. However, we find that this approach also makes the MLLM overly resistant to corrective instructions (i.e., stubborn even if it is wrong). To alleviate this trade-off, we propose Sycophantic Reflective Tuning (SRT), which enables the MLLM to engage in reflective reasoning, allowing it to determine whether a user's instruction is misleading or corrective before drawing a conclusion. After applying SRT, we observe a significant reduction in sycophantic behavior toward misleading instructions, without resulting in excessive stubbornness when receiving corrective instructions.
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