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Finetuning vs RAG
Manage episode 442741670 series 3601172
Yogendra Miraje에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Yogendra Miraje 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Large language models (LLMs) excel at various tasks due to their vast training datasets, but their knowledge can be static and lack domain-specific nuance. Researchers have explored methods like fine-tuning and retrieval-augmented generation (RAG) to address these limitations.
Fine-tuning involves adjusting a pre-trained model on a narrower dataset to enhance its performance in a specific domain. RAG, on the other hand, expands LLMs' capabilities, especially in knowledge-intensive tasks, by using external knowledge sources.
This episode discusses a research paper comparing fine-tuning and RAG as methods for injecting knowledge into LLMs to improve their accuracy in answering factual questions. The authors evaluated these methods on various knowledge-intensive tasks using popular open-source LLMs (Llama2-7B, Mistral-7B, and Orca2-7B), drawing data from the MMLU benchmark and a custom-created current events dataset.
Resources:
https://arxiv.org/pdf/2312.05934
8 에피소드
공유
