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What is Retrieval Augmented Generation?
RAG is a technique to enhance LLMs' potential by exposure to external knowledge sources. Instead of being bounded by only its internal parameters, LLMs empowered by RAG could fetch relevant information from external databases, knowledge graphs, or in some cases, from the internet in real time. RAG operations take place in two main stages:
Advantages of RAG
RAG is a robust technique that promises to overcome some of the critical drawbacks associated with LLMs by rooting the responses in external knowledge. Research in this domain is still open, and RAG is expected to pave the way for more sophisticated and reliable generative AI systems. This optimism is necessary for these systems to prioritize fairness, transparency, and robustness in many contexts, ensuring their responsible and beneficial use in the future.
Limitations of RAG
Despite all these positives, RAG is not a panacea and does have a couple of limitations:
Beyond RAG: The Future of Grounded Generation
While RAG provides an essential advancement in grounding generative AI, it is important to note that several areas remain topics of ongoing work. Newer techniques are being developed that go beyond simple retrieval augmentation. Some examples of these techniques include:
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The post Meta AI and NYU Researchers Propose E-RLHF to Combat LLM Jailbreaking appeared first on MarkTechPost.
