Papers by Zahra Dehghanighobadi
Can LLMs Explain Themselves Counterfactually? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Explanations are an important tool for gaining insights into model behavior, calibrating user trust, and ensuring compliance. |
| Approach: | They propose to use self-explanation to prompt models to explain outputs . they find that LLMs struggle to generate SCEs - their prediction often does not agree with their own counterfactual reasoning. |
| Outcome: | The proposed methods can generate SCEs across families, sizes, temperatures, and datasets. |