Papers by Rajashekar Maragoud
REIC: RAG-Enhanced Intent Classification at Scale (2025.emnlp-industry)
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Ziji Zhang, Michael Yang, Zhiyu Chen, Yingying Zhuang, Shu-Ting Pi, Qun Liu, Rajashekar Maragoud, Vy Nguyen, Anurag Beniwal
| Challenge: | Accurate intent classification is critical for efficient routing in customer service . however, as companies expand their product lines, intent classification faces scalability challenges . |
| Approach: | They propose a retrieval-augmented generation Enhanced Intent Classification approach which leverages retrieval augmented generation to integrate relevant knowledge into a model. |
| Outcome: | The proposed approach outperforms fine-tuning, zero-shot, and few-shot methods on real-world datasets. |
LLM-Based Dialogue Labeling for Multiturn Adaptive RAG (2025.emnlp-industry)
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Zhiyu Chen, Biancen Xie, Sidarth Srinivasan, Manikandarajan Ramanathan, Rajashekar Maragoud, Qun Liu
| Challenge: | Retrieval-Augmented Generation (RAG) models integrate large language models with external knowledge retrieval . however, building multi-turn RAG-based chatbots for real-world customer service requires additional complexities. |
| Approach: | They propose methods to automatically generate labels for adaptive retrieval components using real customer-agent dialogue data. |
| Outcome: | The proposed method generates labels for components using real customer-agent dialogue data. |