Sufficient Context: A New Lens on Retrieval Augmented Generation Systems
Topics: AI (Deep Learning), AIOverviews, E-E-A-T, LLMO / GEO, Prompt Engineering, Retrieval Augmented Generation (RAG)
This Google research paper examines the performance of various large language models (LLMs) in question-answering tasks, particularly focusing on their ability to handle sufficient and insufficient context scenarios. The study evaluates several leading models including GPT-4, Gemini 1.5 Pro, and Claude 3.5 Sonnet, using datasets like FreshQA, HotpotQA, and Musique. The research introduces evaluation metrics and prompting strategies to assess model performance in terms of correct answers, hallucinations, and abstentions.
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