title: Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems
summary: Enterprise search systems often struggle to retrieve accurate,
  domain-specific information due to semantic mismatches and overlapping
  terminologies. These issues can degrade the performance of downstream
  applications such as knowledge management, customer support, and
  retrieval-augmented generation agents. To address this challenge, we propose a
  scalable hard-negative mining framework tailored specifically for
  domain-specific enterprise data. Our approach dynamically selects semantically
  challenging but contextually irrelevant documents to enhance deployed
  re-ranking models.
    Our method integrates diverse embedding models, performs dimensionality
  reduction, and uniquely selects hard negatives, ensuring computational
  efficiency and semantic precision. Evaluation on our proprietary enterprise
  corpus (cloud services domain) demonstrates substantial improvements of 15\% in
  MRR@3 and 19\% in MRR@10 compared to state-of-the-art baselines and other
  negative sampling techniques. Further validation on public domain-specific
  datasets (FiQA, Climate Fever, TechQA) confirms our method's generalizability
  and readiness for real-world applications.
paper_authors._id: 6838949f702f865b81701ff0
paper_authors.name: Hansa Meghwani
paper_authors._id: 6838949f702f865b81701ff1
paper_authors.name: Amit Agarwal
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-30T23:03:01.880Z
paper_authors._id: 6321a50a6b1992383fa00db1
paper_authors.avatarUrl: /avatars/92311b4a5e8bcc8f5477747621334fc8.svg
paper_authors.fullname: Amit Agarwal
paper_authors.type: user
paper_authors.user: amitbcp
paper_authors._id: 6838949f702f865b81701ff2
paper_authors.name: Priyaranjan Pattnayak
paper_authors._id: 6838949f702f865b81701ff3
paper_authors.name: Hitesh Laxmichand Patel
paper_authors._id: 6838949f702f865b81701ff4
paper_authors.name: Srikant Panda
paper_title: Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems
paper_summary: Enterprise search systems often struggle to retrieve accurate,
  domain-specific information due to semantic mismatches and overlapping
  terminologies. These issues can degrade the performance of downstream
  applications such as knowledge management, customer support, and
  retrieval-augmented generation agents. To address this challenge, we propose a
  scalable hard-negative mining framework tailored specifically for
  domain-specific enterprise data. Our approach dynamically selects semantically
  challenging but contextually irrelevant documents to enhance deployed
  re-ranking models.
    Our method integrates diverse embedding models, performs dimensionality
  reduction, and uniquely selects hard negatives, ensuring computational
  efficiency and semantic precision. Evaluation on our proprietary enterprise
  corpus (cloud services domain) demonstrates substantial improvements of 15\% in
  MRR@3 and 19\% in MRR@10 compared to state-of-the-art baselines and other
  negative sampling techniques. Further validation on public domain-specific
  datasets (FiQA, Climate Fever, TechQA) confirms our method's generalizability
  and readiness for real-world applications.
paper_ai_summary: A scalable hard-negative mining framework enhances domain-specific enterprise search by dynamically selecting semantically challenging irrelevant documents, improving re-ranking models' performance.
paper_ai_keywords: hard-negative mining domain-specific enterprise data semantically challenging documents contextually irrelevant documents re-ranking models embedding models dimensionality reduction MRR@3 MRR@10 FiQA Climate Fever TechQA