Demystifying AI: bridging the explainability gap in LLMs

• Main Author: Chan, Darren Inn Siew
• Other Authors: Erik Cambria
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; Retrieval augmented generation; Large language models; Explainability of AI; RAG; LLM; XAI; Sustainability reports auditing; Explainable AI
• Online Access: https://hdl.handle.net/10356/175340
• Institution: Nanyang Technological University

This project looks at the exploration of Retrieval-Augmented Generation (RAG) with large language models (LLMs) to try and improve the explainability of AI systems within specialized domains, such as auditing sustainability reports. This project would focus on the development of a Proof of Concept (...