Improving Generation Accuracy and Reducing LLM Hallucination Through Retrieval-Augmented Generation (RAG)

Generative LLMs hallucinate and underperform in certain content generation tasks. This has motivated researchers to seek viable means to improve model response quality and reliability. One method currently used to address this challenge is retrieval augmented generation (RAG). RAG is an hybrid method that enhances performance through combination of the strengths of retrieval-based and generation-based models to produce responses that are accurate and contextually relevant to the prompt or query issued by user. In this comparison, works that apply RAG to enhance performance in real-world benchmarks are highlighted. With newer LLMs emerging, and underexplored problems in natural language processing being identified, the need for improved RAG methodology to enhance performance cannot be over emphasized.