自监督序列模型世界模型中的涌现线性表示
Emergent Linear Representations in World Models of Self-Supervised Sequence Models
Neel Nanda, Andrew Lee, Martin Wattenberg
AI 速览：作者报告在自监督序列模型（如Othello棋类模型）的世界模型中发现棋盘状态的线性表示，并可通过向量算术控制模型行为。
How do sequence models represent their decision-making process? Prior work suggests that Othello-playing neural network learned nonlinear models of the board state (Li et al., 2023). In this work, we provide evidence of a closely related linear representation of the board. In particular, we show that probing for "my colour" vs. "opponent's colour" may be a simple yet powerful way to interpret the model's internal state. This precise understanding of the internal representations allows us to control the model's behaviour with simple vector arithmetic. Linear representations enable significant interpretability progress, which we demonstrate with further exploration of how the world model is computed.