Papers by Shilpa Bhagavath

2 papers
Benchmarking Deep Search over Heterogeneous Enterprise Data (2025.emnlp-industry)

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Challenge: Existing methods struggle to conduct deep searches and retrieve all necessary evidence.
Approach: They propose a benchmark for evaluating deep search, a retrieval-augmented generation that requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources.
Outcome: The proposed benchmarks show that even the best-performing agentic RAG methods achieve an average performance score of 32.96 on the benchmark.
Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents (2025.findings-acl)

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Challenge: Existing workflow extraction methods for service agents are time-consuming and outdated, causing inconsistent and inconsistent results.
Approach: They propose a framework for extracting and evaluating dialog workflows from historical interactions.
Outcome: The proposed framework improves workflow extraction by 12.16% over baseline.

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