이번 블로그 포스트는 논문 리뷰는 아니고, Meta에서 운영하는 블로그 글의 리뷰입니다.LLaMa를 개발한 Meta가 어떻게 Open Source Large Language Models (LLMs)를 활용할 수 있을지,Part1: Methods for adapting large language models,Part2: To fine-tune or not to fine-tune,Part3: How to fine-tune: Focus on effective datasts로 나눠 설명하고 있는데, 이 내용을 좀 간추려보려 해요.논문에 비해서 훨씬 읽기 쉬운 글이니, 처음 LLM 모델을 접할 때 읽으면 좋을 것 같습니다! 먼저 각 part에 대해 요약해서 말하자면, part1에서는 LLM 모델의 활용을 개괄적으..

Gupta, A., Shirgaonkar, A., Balaguer, A. D. L., Silva, B., Holstein, D., Li, D., ... & Benara, V. (2024). RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture. arXiv preprint arXiv:2401.08406.https://arxiv.org/abs/2401.08406 RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on AgricultureThere are two common ways in which developers are incorporating proprietary and..

Ovadia, O., Brief, M., Mishaeli, M., & Elisha, O. (2023). Fine-tuning or retrieval? comparing knowledge injection in llms. arXiv preprint arXiv:2312.05934.https://arxiv.org/abs/2312.05934 Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMsLarge language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to an..