Papers by Akshay Jagatap
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. |
PEARL: Preference Extraction with Exemplar Augmentation and Retrieval with LLM Agents (2024.emnlp-industry)
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| Challenge: | Existing systems specialize in extracting customer preferences from standalone queries . absence of a conversational interface often leaves customers feeling the need for humanlike assistance . |
| Approach: | They propose a shopping assistant chatbot that extracts customer preferences as key-value filters from a multi-turn conversation on an e-commerce website. |
| Outcome: | The proposed solution improves performance on exact match by 10% compared to baselines and improves inference latency by 1%. |