The paper "Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era" by Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, and Mengnan Du, explores the application of Explainable AI (XAI) techniques to Large Language Models (LLMs). The authors define Usable XAI as enhancing LLMs and AI systems through explainability, and enhancing XAI frameworks using LLMs. They introduce 10 strategies for achieving these goals, including: 1. **LLM Diagnosis via Attribution Methods**: Using attribution methods to diagnose and enhance LLMs. 2. **LLM Diagnosis and Enhancement via Interpreting Model Components**: Understanding and improving LLM components like self-attention and feed-forward layers. 3. **LLM Debugging with Sample-based Explanation**: Tracing LLM outputs to specific training samples. 4. **Explainability for Trustworthy LLMs and Human Alignment**: Ensuring security, fairness, toxicity, and truthfulness in LLMs. 5. **LLM Enhancement via Explainable Prompting**: Using Chain-of-Thought and knowledge-enhanced prompts. 6. **LLM Enhancement via Knowledge-Augmented Prompting**: Augmenting training data with explicit knowledge. 7. **Training Data Augmentation with Explanation**: Guiding data augmentation with explanations. 8. **Generating User-Friendly Explanation for XAI**: Creating user-friendly explanations for LLMs. 9. **LLMs for Interpretable AI System Design**: Designing interpretable AI workflows with LLMs. 10. **Emulating Humans with LLMs for XAI**: Using LLMs to emulate human cognition in XAI evaluation. The paper also discusses the challenges and open questions in each area, providing case studies to illustrate the practical application of these strategies. The authors aim to bridge the gap between theoretical XAI methods and their real-world implementation, particularly in the context of LLMs, and provide detailed case studies and open-sourced code to facilitate future research.The paper "Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era" by Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, and Mengnan Du, explores the application of Explainable AI (XAI) techniques to Large Language Models (LLMs). The authors define Usable XAI as enhancing LLMs and AI systems through explainability, and enhancing XAI frameworks using LLMs. They introduce 10 strategies for achieving these goals, including: 1. **LLM Diagnosis via Attribution Methods**: Using attribution methods to diagnose and enhance LLMs. 2. **LLM Diagnosis and Enhancement via Interpreting Model Components**: Understanding and improving LLM components like self-attention and feed-forward layers. 3. **LLM Debugging with Sample-based Explanation**: Tracing LLM outputs to specific training samples. 4. **Explainability for Trustworthy LLMs and Human Alignment**: Ensuring security, fairness, toxicity, and truthfulness in LLMs. 5. **LLM Enhancement via Explainable Prompting**: Using Chain-of-Thought and knowledge-enhanced prompts. 6. **LLM Enhancement via Knowledge-Augmented Prompting**: Augmenting training data with explicit knowledge. 7. **Training Data Augmentation with Explanation**: Guiding data augmentation with explanations. 8. **Generating User-Friendly Explanation for XAI**: Creating user-friendly explanations for LLMs. 9. **LLMs for Interpretable AI System Design**: Designing interpretable AI workflows with LLMs. 10. **Emulating Humans with LLMs for XAI**: Using LLMs to emulate human cognition in XAI evaluation. The paper also discusses the challenges and open questions in each area, providing case studies to illustrate the practical application of these strategies. The authors aim to bridge the gap between theoretical XAI methods and their real-world implementation, particularly in the context of LLMs, and provide detailed case studies and open-sourced code to facilitate future research.