Papers by Ankur Taly
An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning (2025.findings-naacl)
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| Challenge: | Existing approaches to fine-tune foundational models on new tasks or domains are costly and time-consuming. |
| Approach: | They propose a sampling scheme that prioritizes rehearsal of "collateral damage" samples . the scheme is computationally efficient and easy to implement, they say . |
| Outcome: | a new approach prioritizes rehearsal of “collateral damage” samples outperforms other continual learning methods. |
Did the Model Understand the Question? (P18-1)
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| Challenge: | Using the notion of “attribution,” deep learning models often ignore important question terms. |
| Approach: | They propose techniques to analyze the sensitivity of a deep learning model to question words . they use attribution to generate adversarial questions using visual and tabular questions . |
| Outcome: | The proposed techniques reduce the accuracy of a visual question answering model by 61.1% and that of 'tabular' question answering models by 3.3%. |