XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models
It provides a comprehensive overview for AI researchers and practitioners on integrating XAI with LLMs, but is incremental as a survey paper.
This survey addresses the need for interpretability in Large Language Models (LLMs) by examining the relationship between LLM research and Explainable AI (XAI), advocating for a balanced approach that values transparency alongside performance improvements.
In this survey, we address the key challenges in Large Language Models (LLM) research, focusing on the importance of interpretability. Driven by increasing interest from AI and business sectors, we highlight the need for transparency in LLMs. We examine the dual paths in current LLM research and eXplainable Artificial Intelligence (XAI): enhancing performance through XAI and the emerging focus on model interpretability. Our paper advocates for a balanced approach that values interpretability equally with functional advancements. Recognizing the rapid development in LLM research, our survey includes both peer-reviewed and preprint (arXiv) papers, offering a comprehensive overview of XAI's role in LLM research. We conclude by urging the research community to advance both LLM and XAI fields together.