Long Context RAG Performance of LLMs

lara_rachidi — Mon, 02 Sep 2024 20:02:32 GMT

In this video, we explore an article by the Mosaic AI research team on the performance of LLMs with extended context lengths. With models like Anthropic's Cloud (200,000 tokens), GPT-4 Turbo, and Google's Gemini pushing context limits further than ever before, we ask: Is increased context length better for RAG performance? Key Topics Covered: 1. The debate: Do we still need retrieval-augmented generation (RAG) systems with extended context lengths? 2. Two major limitations of long-context LLMs: the "Lost in the Middle" problem and effective context length. 3. Over 2000 experiments on 13 open-source and commercial LLMs—what did we learn? 4. Key findings on optimal context length, performance saturation, and unique model failures. 5. A closer look at datasets used, evaluation metrics, and the implications for AI developers. Don't miss out on insights that could redefine how you approach building RAG apps with LLMs! Make sure to like, subscribe, and hit the bell icon to stay updated with our latest videos! Key Moments: - [00:06] - Introduction to the research on long context lengths in LLMs - [00:36] - Debate: Do we still need RAG systems with long-context LLMs? - [02:36] - Two key limitations of long-context LLMs - [03:40] - Findings from 2000+ experiments on 13 LLMs - [04:42] - The optimal context length for different tasks - [07:03] - Retrieving more documents for better results - [08:14] - Different dataset structures and their impact on LLM performance - [09:26] - Context length saturation and inflection points - [11:23] - Unique failure types across different LLMs - [12:56] - Conclusion: The need for evaluation tools

article Long Context RAG Performance of LLMs in Databricks TV

Long Context RAG Performance of LLMs