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Professionals derive their efficacy and influence from their profound expertise and unwavering credibility. The foundation of expertise across disciplines lies in the ability to comprehend diverse sources of domain knowledge and distill valuable insights from cutting-edge discoveries. By transparently acknowledging prior contributions and innovating upon them, we not only fortify our intellectual bases but also rectify logical discrepancies. Our ambition is to design a Large Language Model framework that bridges the knowledge gap by offering expert insights to novices and fostering the inspirational journey of experts.

To incorporate recent innovations and latest updates, our approach initiates by retrieving multiple relevant documents. A key step involves an adequate blending of both parametric and non-parametric information. For precise evidence extraction, our generative model identifies evidential paragraphs. Such an associative selection is more advantageous in specialized domains than compressing extensive documents, as summaries might overlook crucial contexts vital for a wide range of user inquiries. Then we systematically integrate selected evidence to furnish holistic answers. Our framework draws the strength from its hierarchical reference mechanism, empowering users to scrutinize any hallucinations or potential inaccuracies when the generated content seems questionable. Through human evaluations across various expertise and credibility metrics, we illustrate the capability and scalability of the EXAONE framework by LG AI Research.

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