LLM as a Judge to Validate the Accuracy of Generated Content

Large Language Models (LLMs) like GPT-4 are being have found many use cases. A particular strength of these models, however, is the abstractive summarization of long, complicated text. This application presents the user with a problem though - how can you be sure that the summary is correct? The simple solution is to read the entire document being summarised - but naturally, that defeats the purpose of generating this summary. At PwC, I have implemented a novel technique that uses LLMs as judges to validate the accuracy of generated content through Retrieval-Augmented Generation (RAG).

RAG combines the strengths of retrieval-based and generative models. When an LLM generates a summary, we can now use RAG to cross-reference the summary with relevant source documents. This process involves retrieving pertinent information from the source material and comparing it with the generated content to identify discrepancies and ensure factual accuracy.

This approach not only improves the quality of the content but also instills confidence in users, making them more likely to adopt and integrate AI into their business operations. By demonstrating the effectiveness of RAG in producing reliable and trustworthy content, we empower businesses to harness the full potential of AI, fostering innovation and efficiency.

I have attached a demonstration of the tool that I built, presented by one of my colleagues, Timothy Chapman. This shows the LLM-as-a-judge tool being presented in a front end application that I also put together via Streamlit.

In conclusion, employing LLMs as judges using RAG for content validation holds significant potential. It can streamline the review process, enhance content reliability, and combat misinformation. As AI technology advances, the role of LLMs in ensuring content accuracy through RAG will likely become even more critical.