Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems

arXiv:2403.09727v1 Announce Type: cross
Abstract: The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the development of G-LLM-based knowledge systems. In our study, using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine the performance of RAG and FN for the GPT-J-6B, …

abstract arxiv bing chatgpt cs.ai cs.cl cs.lg development fine-tuning gemini generative knowledge language language models large language large language models llm opportunities performance rag retrieval retrieval-augmented systems type types