Papers by Sanjay Agrawal
KD-Boost: Boosting Real-Time Semantic Matching in E-commerce with Knowledge Distillation (2023.emnlp-industry)
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| Challenge: | Existing SOTA techniques for semantic matching are mostly based on Siamese networks. |
| Approach: | They propose a novel knowledge distillation algorithm designed for real-time semantic matching . they train low latency accurate student models by leveraging soft labels from a teacher model . |
| Outcome: | The proposed algorithm outperforms teacher and SOTA knowledge distillation benchmarks on e-commerce datasets. |
Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End SOP Automation in E-Commerce Operations (2026.acl-industry)
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| Challenge: | Existing Large Language Models fail to execute multistep operational workflows requiring precise procedural adherence. |
| Approach: | They propose an end-to-end multi-agent framework automating Standard Operating Procedures in e-commerce. |
| Outcome: | The proposed framework achieves 85-97% accuracy and a 94.2% execution consistency in e-commerce . it is based on a human-AI framework that transforms ambiguous documentation into automation-ready specifications . |
Rationale-Guided Distillation for E-Commerce Relevance Classification: Bridging Large Language Models and Lightweight Cross-Encoders (2025.coling-industry)
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| Challenge: | Large-scale e-commerce search systems typically follow a multi-step process to retrieve relevant products for a given query. |
| Approach: | They propose a distillation approach that uses "rationales" generated by Large Language Models to guide smaller cross-encoder models. |
| Outcome: | The proposed model achieves ROC-AUC improvements of 1.4% on 9 multilingual e-commerce datasets, 2.4% on 3 ESCI datasets and 6% on GLUE datasets while being 50 times faster per sample. |
RTSM: Knowledge Distillation with Diverse Signals for Efficient Real-Time Semantic Matching in E-Commerce (2025.naacl-industry)
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| Challenge: | e-commerce product search is a key component of product discovery and sales in ecommerce . high computational demands of large transformer models pose challenges for their deployment in real-time scenarios. |
| Approach: | They propose a framework for real-time semantic matching that leverages both soft labels from a teacher model and ground truth generated from pairwise query-product and query-query signals. |
| Outcome: | The proposed framework outperforms teacher models and state-of-the-art models on e-commerce datasets. |
Multilingual Continual Learning using Attention Distillation (2025.coling-industry)
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| Challenge: | Existing models for Query-product relevance classification are not accurate across multiple languages. |
| Approach: | They propose a multilingual continual learning framework that adds adapters for each new language and incorporates a fusion layer above language-specific adapters. |
| Outcome: | The proposed approach reduces trainable parameters by 80% while outperforming SOTA CL methods on proprietary and external datasets. |