Explaining Language Model Predictions with High-Impact Concepts (2024.findings-eacl)
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| Challenge: | Existing methods to explain large language models (LLMs) are mostly correlational and lack causal features due to compositional nature of languages. |
| Approach: | They propose a framework to provide impact-aware explanations for large language models that are robust to feature changes and influential to the model’s predictions. |
| Outcome: | The proposed explanations improve on real and synthetic tasks and are robust to feature changes and influential to the model’s predictions. |
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