Multi-task retriever fine-tuning for domain-specific and efficient RAG
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generating hallucinated or outdated information.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.
677fdb64a907a4645d4ad7e2
Patrice Béchard
extracted_confirmed
2025-01-09T14:24:03.741Z
607f060442beb4da0f990182
https://cdn-avatars.huggingface.co/v1/production/uploads/607f060442beb4da0f990182/j5W2tLyU6JqkaTf3kv66s.jpeg
Patrice Bechard
user
patricebechard
677fdb64a907a4645d4ad7e3
Orlando Marquez Ayala
extracted_confirmed
2025-01-09T18:53:32.679Z
64820d2bd8662b0714a2a3cd
https://cdn-avatars.huggingface.co/v1/production/uploads/64820d2bd8662b0714a2a3cd/AHp4bGT05PNlaIGue5gDw.png
Orlando Marquez
user
marquezo
Multi-task retriever fine-tuning for domain-specific and efficient RAG
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generating hallucinated or outdated information.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.
Instruction-tuned retrieval encoder addresses domain-specific challenges for efficient, scalable, and fast Retrieval-Augmented Generation (RAG) applications.
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