In LLMs, we come across inaccuracies and hallucinations in what they generate. We have previously discussed the concept of Retrieval Augmented Generation (RAG) where the LLM is integrated to relevant external knowledge in the generation process.
Though this addresses the problem to a large extent, there is an issue here. RAG’s success depends on the accuracy and relevance of the retrieved documents. If the retrieval process fails, we face the inaccuracies in the generative process.
Researchers have devised a pathbreaking Corrective Retrieval Augmented Generation process (CRAG). Here a lightweight Retrieval Evaluator is introduced. It assesses the quality of retrieved documents. The documents could be correct, ambiguous or incorrect. They are subjected to knowledge refinement. They are subjected to knowledge refinement and knowledge searching if they are ambiguous. They are subjected to knowledge searching if they are incorrect. Thus, the documents are corrected to x. These are then generated. This is the dynamic approach of document retrieval. It uses a decompose-recompose algorithm if the documents are sub-optimal. Thus, the generative process gives most relevant and accurate information.
CRAG surfs the vast web resources to augment its knowledge base. It goes beyond static corpus of data.
This is a significant leap forward for the LLMs. It sets a new standard for integrating superficial knowledge in the generative process.
There is fluent text with factual integrity.
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