Papers by Vivek Varadarajan Sembium
Rationale-Guided Distillation for E-Commerce Relevance Classification: Bridging Large Language Models and Lightweight Cross-Encoders (2025.coling-industry)
Copied to clipboard
| 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. |
Multilingual Continual Learning using Attention Distillation (2025.coling-industry)
Copied to clipboard
| 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. |