Papers by Ashwin Srinivasan
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations (2022.findings-acl)
Copied to clipboard
| Challenge: | Dense retrieval (DR) methods first encode texts into a dense embedding space and then conduct text retrieval using efficient nearest neighbor search. |
| Approach: | They propose Momentum adversarial Domain Invariant Representation learning to train a domain classifier that distinguishes source versus target domains and adversarially updates the DR encoder to learn domain invariant representations. |
| Outcome: | The proposed method outperforms baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setting, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. |
Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting (2025.naacl-industry)
Copied to clipboard
Emmanuel Aboah Boateng, Cassiano O Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare, Soundararajan Srinivasan, Victor Dibia
| Challenge: | Concept Distillation (CD) is an automated prompt optimization technique for enhancing weaker models on complex tasks. |
| Approach: | They propose an automatic prompt optimization technique for enhancing weaker models on complex tasks using a base prompt and a strong model to generate reasons for these mistakes. |
| Outcome: | The proposed technique improves weaker models on NL2Code and mathematical reasoning tasks, while preserving performance. |