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Finetuning vs RAG
Manage episode 442741670 series 3601172
المحتوى المقدم من Yogendra Miraje. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Yogendra Miraje أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.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
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