Papers by Zaiwen Feng
Trustworthy and Explainable Causal Representation Learning in Transformers (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to interpretable representation learning rely on masks that weight the significance of input features, but the origin of these masks is uncertain. |
| Approach: | They propose a causal framework that directly learns identifiable representations from attention weights rather than relying on importance masks. |
| Outcome: | The proposed framework learns identifiable and explainable representations from attention weights, rather than masks, and guarantees faithfulness on real-world datasets. |