2025
Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
ICLR 2025poster
Transformer-based models generate hidden states that are difficult to interpret. In this work, we analyze hidden states and modify them at inference, with a focus on motion forecasting. We use linear probing to analyze whether interpretable features are embedded in hidden states. Our experiments rev…