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cs.CL2025
When Actions Teach You to Think: Reasoning-Action Synergy via Reinforcement Learning in Conversational Agents
Mrinal Rawat, Arkajyoti Chakraborty, Neha Gupta +1
Supervised fine-tuning (SFT) has emerged as one of the most effective ways to improve the performance of large language models (LLMs) in downstream tasks. However, SFT can have dif…
cs.CL2024
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning
Mrinal Rawat, Hithesh Sankararaman, Victor Barres
Deriving value from a conversational AI system depends on the capacity of a user to translate the prior knowledge into a configuration. In most cases, discovering the set of releva…
cs.CL2024
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models
Ambuje Gupta, Mrinal Rawat, Andreas Stolcke +1
Retrieval augmented generation (RAG) pipelines are commonly used in tasks such as question-answering (QA), relying on retrieving relevant documents from a vector store computed usi…