量子位·2025-07-20 05:08Core Viewpoint - The research conducted by Google DeepMind and University College London reveals that large language models (LLMs) exhibit conflicting behaviors of being both confident and self-doubting, influenced by their sensitivity to opposing feedback [2][3][21]. Group 1: Model Behavior - LLMs tend to maintain their initial answers when they can see them, reflecting a human-like tendency to uphold one's viewpoint after making a decision [11][12]. - Conversely, when the initial answer is hidden, LLMs are more likely to change their answers, indicating an excessive sensitivity to opposing suggestions, even if those suggestions are incorrect [13][21]. - This behavior diverges from human cognition, as humans typically do not easily abandon their correct conclusions based on misleading information [15][21]. Group 2: Experimental Design - The study involved a two-round experiment where LLMs were first presented with a binary choice question and then received feedback from a fictional suggestion LLM [7][8]. - Key variables included whether the initial answer was visible to the responding LLM, which significantly affected the final decision-making process [9][10]. Group 3: Reasons for Inconsistent Behavior - The inconsistency in LLM responses is attributed to several factors: - Over-reliance on external feedback due to reinforcement learning from human feedback (RLHF), leading to a lack of independent judgment regarding the reliability of information [19][21]. - Decision-making based on statistical pattern matching rather than logical reasoning, making LLMs susceptible to misleading signals [19][21]. - The absence of a robust memory mechanism that would allow for deeper reasoning, resulting in a tendency to be swayed by opposing suggestions when the initial answer is not visible [21][22].