Summary of Long Context Rag Performance Of Large Language Models, by Quinn Leng et al.
Long Context RAG Performance of Large Language Models
by Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, Michael Carbin
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study explores the impact of increased context length on Retrieval Augmented Generation (RAG) performance across various Large Language Models (LLMs). The researchers ran RAG workflows with context lengths ranging from 2,000 to 128,000 tokens and reported findings on three domain-specific datasets. The results show that while retrieving more documents can improve accuracy, only a handful of state-of-the-art LLMs can maintain consistent performance at long contexts above 64k tokens. The study also identifies distinct failure modes in long context scenarios, highlighting areas for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists investigated how Large Language Models (LLMs) perform when given more information to work with. They tested many popular LLMs on three specific tasks and found that while getting more data can help, only the newest and best models can do well when working with really long pieces of text. The researchers also discovered some problems that these models have when dealing with very long texts, which they think is important for future studies. |
Keywords
» Artificial intelligence » Context length » Rag » Retrieval augmented generation
