Extend the knowledge of your Large Language Model with RAG
Thanh Long Phan, Fabian Dechent
Large Language Models (LLMs) have rapidly gained popularity in Natural Language tasks due to their remarkable human-like ability to understand and generate text. Amidst great advances, there are still challenges to be solved on the way to building perfectly reliable assistants. LLMs are known to make up answers, often producing text that adheres to the expected style, but lacks accuracy or factual grounding. Generated words and phrases are chosen as they are likely to follow previous text, where the likelihood is adjusted to fit the training corpus as closely as possible. This gives rise to the possibility that a piece of information is outdated, if the corpus is not updated and the model retrained. Or that it is just factually incorrect, while the generated words have the quality of sounding correct and can be matched to the required genre. The core problem here is that the LLM does not know, what it does not know. In addition, even if a piece of information is correct, it is hard to track its source in order to enable fact-checking. In this article, we introduce RAG (Retrieval-Augmented Generation) as a method to address both problems and which thus aims to enhance the reliability and accuracy of information generated by LLMs. Note: If you are interested in a 30min conversation with our dedicated LLM and NLP contact person, please take a look at our free NLP Talk offering.
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