Summary of Llms For Xai: Future Directions For Explaining Explanations, by Alexandra Zytek et al.
LLMs for XAI: Future Directions for Explaining Explanations
by Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni
First submitted to arxiv on: 9 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 The paper investigates using Large Language Models (LLMs) to transform Machine Learning (ML) explanations into natural narratives, enhancing the interpretability and usability of Explainable Artificial Intelligence (XAI). It focuses on refining existing XAI algorithm explanations rather than directly explaining ML models. The research directions explored include defining evaluation metrics, prompt design, comparing LLM models, further training methods, and integrating external data. Initial experiments and user studies suggest that LLMs offer a promising approach for improving XAI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special computers called Large Language Models to help people understand how machines make decisions. Right now, these explanations are often hard to read and understand. The researchers want to fix this by using the computers to turn complex explanations into easy-to-understand stories. They suggest ways to do this better, like making a list of what’s important or comparing different types of computer models. Early tests show that this approach might really help people get more out of these explanations. |
Keywords
» Artificial intelligence » Machine learning » Prompt
