Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era

Explainability for LLMs requires fundamentally new approaches: Traditional XAI methods cannot be directly applied to Large Language Models due to their complexity and advanced capabilities, necessitating a significant transformation in how we explain these systems to users.

Explainability's purpose is shifting from transparency to practical utility: Rather than simply "opening the black box," XAI for LLMs should focus on actively enhancing productivity and real-world applicability, making explanations useful for actual deployment scenarios rather than just satisfying curiosity about how models work.

LLMs can improve explainability itself: The paper identifies a reciprocal relationship where LLMs' conversation and generation abilities can be leveraged to enhance XAI techniques themselves, creating a feedback loop where better language models lead to better explanations.

The paper provides 10 actionable strategies for usable XAI: Rather than remaining theoretical, the research introduces specific techniques with practical case studies demonstrating how to obtain and leverage explanations in real-world LLM applications.

Explainability becomes more critical as LLMs are deployed widely: As these powerful systems are increasingly used across diverse applications, understanding and explaining their decision-making processes becomes essential for responsible deployment and building appropriate user trust.