Papers by Rohan Nair
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals (2021.emnlp-main)
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| Challenge: | Recent research in interpretability of neural models has yielded numerous token attribution techniques, but it is hard to evaluate whether these explanations are faithful. |
| Approach: | They propose to use pairwise attributions to connect outputs to high-level model behavior to examine how well different attribution techniques align with this assumption on realistic counterfactuals in the case of reading comprehension (RC). |
| Outcome: | The proposed methods are better suited to RC than token-level attributions across different RC settings, and the best performance comes from a modification that was proposed to an existing pairwise attribution method. |