Summary of Usable Xai: 10 Strategies Towards Exploiting Explainability in the Llm Era, by Xuansheng Wu et al.
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, Mengnan Du, Ninghao Liu
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on Explainable AI (XAI) techniques that provide insights into the workings of Large Language Models (LLMs). The extension of XAI to LLMs requires a significant transformation in methodologies due to their complexity and advanced capabilities. Unlike traditional machine learning models, LLMs can reciprocally enhance XAI. This paper introduces Usable XAI in the context of LLMs by analyzing how XAI can benefit LLMs and AI systems, as well as how LLMs can contribute to the advancement of XAI. The authors propose 10 strategies for usable XAI, including key techniques and challenges. Case studies demonstrate how to obtain and leverage explanations from LLMs. The code used in this paper is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on making Large Language Models (LLMs) more understandable by people. LLMs are complex AI models that can do many tasks, but they are often hard to understand. To make them easier to use and trust, we need new techniques called Explainable AI (XAI). This paper shows how XAI can be used with LLMs to improve their usefulness in real-world situations. The authors propose 10 ways to make XAI work better with LLMs, including examples of how this can be done. |
Keywords
* Artificial intelligence * Machine learning
