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What is Retrieval-Augmented Fine-Tuning (RAFT)?

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What is Retrieval-Augmented Fine-Tuning (RAFT)?

Retrieval Augmented Fine Tuning (RAFT) combines the strengths of retrieval-based and fine-tuning methods to improve domain-specific performance in language models. It trains models to better select relevant documents and reduces hallucinations, making AI more robust and scalable for enterprise applications. #RAFT #RetrievalAugmentedGeneration

Keypoints :

  • RAFT is a hybrid technique developed at UC Berkeley that enhances language model performance in specialized settings.
  • It involves training the model to use external documents effectively during inference, similar to teaching how to fish.
  • The training process uses query, relevant core documents, tangent documents, and target answers to improve document filtering and accuracy.
  • Including tangent documents teaches the model how to distinguish relevant from irrelevant information, boosting precision.
  • Creating document sets without relevant information helps the model recognize when to rely on intrinsic knowledge and avoid hallucinations.
  • Chain of thought reasoning guides the model to quote specific sources, increasing transparency and reducing overfitting.
  • RAFT results in scalable, robust models suitable for enterprise tasks requiring precise domain knowledge.

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