title: Multi-task retriever fine-tuning for domain-specific and efficient RAG
summary: Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying
  Large Language Models (LLMs), as it can address typical limitations such as
  generating hallucinated or outdated information. However, when building
  real-world RAG applications, practical issues arise. First, the retrieved
  information is generally domain-specific. Since it is computationally expensive
  to fine-tune LLMs, it is more feasible to fine-tune the retriever to improve
  the quality of the data included in the LLM input. Second, as more applications
  are deployed in the same real-world system, one cannot afford to deploy
  separate retrievers. Moreover, these RAG applications normally retrieve
  different kinds of data. Our solution is to instruction fine-tune a small
  retriever encoder on a variety of domain-specific tasks to allow us to deploy
  one encoder that can serve many use cases, thereby achieving low-cost,
  scalability, and speed. We show how this encoder generalizes to out-of-domain
  settings as well as to an unseen retrieval task on real-world enterprise use
  cases.
paper_authors._id: 677fdb64a907a4645d4ad7e2
paper_authors.name: Patrice Béchard
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-09T14:24:03.741Z
paper_authors._id: 607f060442beb4da0f990182
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/607f060442beb4da0f990182/j5W2tLyU6JqkaTf3kv66s.jpeg
paper_authors.fullname: Patrice Bechard
paper_authors.type: user
paper_authors.user: patricebechard
paper_authors._id: 677fdb64a907a4645d4ad7e3
paper_authors.name: Orlando Marquez Ayala
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-09T18:53:32.679Z
paper_authors._id: 64820d2bd8662b0714a2a3cd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64820d2bd8662b0714a2a3cd/AHp4bGT05PNlaIGue5gDw.png
paper_authors.fullname: Orlando Marquez
paper_authors.type: user
paper_authors.user: marquezo
paper_title: Multi-task retriever fine-tuning for domain-specific and efficient RAG
paper_summary: Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying
  Large Language Models (LLMs), as it can address typical limitations such as
  generating hallucinated or outdated information. However, when building
  real-world RAG applications, practical issues arise. First, the retrieved
  information is generally domain-specific. Since it is computationally expensive
  to fine-tune LLMs, it is more feasible to fine-tune the retriever to improve
  the quality of the data included in the LLM input. Second, as more applications
  are deployed in the same real-world system, one cannot afford to deploy
  separate retrievers. Moreover, these RAG applications normally retrieve
  different kinds of data. Our solution is to instruction fine-tune a small
  retriever encoder on a variety of domain-specific tasks to allow us to deploy
  one encoder that can serve many use cases, thereby achieving low-cost,
  scalability, and speed. We show how this encoder generalizes to out-of-domain
  settings as well as to an unseen retrieval task on real-world enterprise use
  cases.
paper_ai_summary: Instruction-tuned retrieval encoder addresses domain-specific challenges for efficient, scalable, and fast Retrieval-Augmented Generation (RAG) applications.
paper_ai_keywords: Retrieval-Augmented Generation Large Language Models hallucinated information outdated information fine-tuning retriever domain-specific separate retrievers instruction fine-tune generalizes out-of-domain unseen retrieval task