@inproceedings{choubey-etal-2025-benchmarking,
title = "Benchmarking Deep Search over Heterogeneous Enterprise Data",
author = "Choubey, Prafulla Kumar and
Peng, Xiangyu and
Bhagavath, Shilpa and
Huang, Kung-Hsiang and
Xiong, Caiming and
Wu, Chien-Sheng",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.34/",
pages = "501--517",
ISBN = "979-8-89176-333-3",
abstract = "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 diverse, sparsed, but related sources. These include documents, meeting transcripts, Slack messages, GitHub, and URLs, which vary in structure and often contain human-to-human interactions. We build it using a synthetic data pipeline that simulates business workflows across product planning, development, and support stages, generating interconnected content with realistic noise and multi-hop questions with guaranteed ground-truth answers. We release our benchmark with both answerable and unanswerable queries, and retrieval pool of 39,190 enterprise artifacts, enabling fine-grained evaluation of long-context LLM and RAG systems. Our experiments reveal that even the best-performing agentic RAG methods achieve an average performance score of 32.96 on our benchmark. With further analysis, we highlight retrieval as the main bottleneck: existing methods struggle to conduct deep searches and retrieve all necessary evidence. Consequently, they often reason over partial context, leading to significant performance degradation."
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%0 Conference Proceedings
%T Benchmarking Deep Search over Heterogeneous Enterprise Data
%A Choubey, Prafulla Kumar
%A Peng, Xiangyu
%A Bhagavath, Shilpa
%A Huang, Kung-Hsiang
%A Xiong, Caiming
%A Wu, Chien-Sheng
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F choubey-etal-2025-benchmarking
%X 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 diverse, sparsed, but related sources. These include documents, meeting transcripts, Slack messages, GitHub, and URLs, which vary in structure and often contain human-to-human interactions. We build it using a synthetic data pipeline that simulates business workflows across product planning, development, and support stages, generating interconnected content with realistic noise and multi-hop questions with guaranteed ground-truth answers. We release our benchmark with both answerable and unanswerable queries, and retrieval pool of 39,190 enterprise artifacts, enabling fine-grained evaluation of long-context LLM and RAG systems. Our experiments reveal that even the best-performing agentic RAG methods achieve an average performance score of 32.96 on our benchmark. With further analysis, we highlight retrieval as the main bottleneck: existing methods struggle to conduct deep searches and retrieve all necessary evidence. Consequently, they often reason over partial context, leading to significant performance degradation.
%U https://aclanthology.org/2025.emnlp-industry.34/
%P 501-517
Markdown (Informal)
[Benchmarking Deep Search over Heterogeneous Enterprise Data](https://aclanthology.org/2025.emnlp-industry.34/) (Choubey et al., EMNLP 2025)
ACL
- Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath, Kung-Hsiang Huang, Caiming Xiong, and Chien-Sheng Wu. 2025. Benchmarking Deep Search over Heterogeneous Enterprise Data. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 501–517, Suzhou (China). Association for Computational Linguistics.