Papers by Prakash Mandayam Comar
RxLens: Multi-Agent LLM-powered Scan and Order for Pharmacy (2025.naacl-industry)
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| Challenge: | paper prescriptions are difficult for customers to interpret and are often unstructured, handwritten, and illegible. |
| Approach: | They propose a multi-step Large Language Model-based solution for automated pharmacy cart construction. |
| Outcome: | The proposed solution can yield up to 19% - 40% and 11% - 26% increase in Recall@3 relative to SOTA methods. |
Reinforcement Learning for Adversarial Query Generation to Enhance Relevance in Cold-Start Product Search (2025.acl-industry)
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| Challenge: | Existing methods do not incorporate feedback from the query relevance model, limiting their ability to generate queries that enhance product retrieval. |
| Approach: | They propose an adversarial reinforcement learning framework that exposes weaknesses in query classification models by creating synthetic queries that augment the classifier's training set. |
| Outcome: | The proposed framework improves query generation performance on public datasets and on proprietary datasets. |
In-Context Reinforcement Learning with Retrieval-Augmented Generation for Text-to-SQL (2025.coling-main)
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| Challenge: | Existing methods of synthetic query generation generate mostly simple queries which might not be sufficiently representative of complex, real world queries. |
| Approach: | They propose to use large language models to fine tune query generation to produce complex queries that practitioners may pose during inference. |
| Outcome: | The proposed framework achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation. |