Summary of A Comprehensive Survey Of Retrieval-augmented Generation (rag): Evolution, Current Landscape and Future Directions, by Shailja Gupta et al.
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
by Shailja Gupta, Rajesh Ranjan, Surya Narayan Singh
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 comprehensive study tracks the evolution of Retrieval-Augmented Generation (RAG) from foundational concepts to the current state-of-the-art. By combining retrieval mechanisms with generative language models, RAG addresses limitations of Large Language Models (LLMs). The paper reviews the basic architecture of RAG, focusing on how retrieval and generation are integrated for knowledge-intensive tasks. Key innovations in retrieval-augmented language models and applications across question-answering, summarization, and knowledge-based tasks are explored. Novel methods for improving retrieval efficiency are discussed, along with ongoing challenges like scalability, bias, and ethical concerns. Future research directions focus on robustness, scope expansion, and societal implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Retrieval-Augmented Generation (RAG) works and what it’s good for. RAG is a way to make language models better by combining them with other methods that help find information. The paper talks about how this works and shows examples of how it can be used in things like answering questions, summarizing text, and using knowledge to do tasks. It also looks at some of the challenges of using RAG, like making sure it’s fair and doesn’t have biases. Overall, the study is trying to help people understand what RAG is and what it might be able to do in the future. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation » Summarization
