Ashwin Srinivasan — accepted AI-conference papers

2025

Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting

Emmanuel Aboah Boateng, Cassiano O Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare +2

NAACL 2025industry

Hand-crafting high quality prompts to optimize the performance of language models is a complicated and labor-intensive process. Furthermore, when migrating to newer, smaller, or weaker models (possibly due to latency or cost gains), prompts need to be updated to re-optimize the task performance. We…

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2024

Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback

Shreyas Bhat Brahmavar, Ashwin Srinivasan, Tirtharaj Dash, Sowmya Ramaswamy Krishnan, Lovekesh Vig, Arijit Roy +1

AAAI 2024technical

Large Language Models (LLMs) can be used as repositories of biological and chemical information to generate pharmacological lead compounds. However, for LLMs to focus on specific drug targets typically requires experimentation with progressively more refined prompts. Results thus become dependent no…

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2022

Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations

Ji Xin, Chenyan Xiong, Ashwin Srinivasan, Ankita Sharma, Damien Jose, Paul Bennett

ACL 2022findings

Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search. This requires strong locality properties from the representation space, e.g., close allocations of each small group of relevant texts, which are hard…

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