8 papers · 1 filter
Dont Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination
Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit +5
Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a scalable Enterpri…
UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG
Xiangyu Peng, Can Qin, Zeyuan Chen +3
Multimodal retrieval-augmented Generation (MM-RAG) is a key approach for applying large language models (LLMs) and agents to real-world knowledge bases, yet current evaluations are…
Benchmarking Deep Search over Heterogeneous Enterprise Data
Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath +3
We present a new benchmark for evaluating Deep Search--a realistic and complex form of retrieval-augmented generation (RAG) that requires source-aware, multi-hop reasoning over div…
BingoGuard: LLM Content Moderation Tools with Risk Levels
Fan Yin, Philippe Laban, Xiangyu Peng +7
Malicious content generated by large language models (LLMs) can pose varying degrees of harm. Although existing LLM-based moderators can detect harmful content, they struggle to as…
Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents
Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath +3
Automated service agents require well-structured workflows to provide consistent and accurate responses to customer queries. However, these workflows are often undocumented, and th…
Unanswerability Evaluation for Retrieval Augmented Generation
Xiangyu Peng, Prafulla Kumar Choubey, Caiming Xiong +1
Existing evaluation frameworks for retrieval-augmented generation (RAG) systems focus on answerable queries, but they overlook the importance of appropriately rejecting unanswerabl…