Summary of Explingo: Explaining Ai Predictions Using Large Language Models, by Alexandra Zytek et al.
Explingo: Explaining AI Predictions using Large Language Models
by Alexandra Zytek, Sara Pido, Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Machine learning model predictions generated by Explainable AI (XAI) techniques like SHAP are crucial for decision-making purposes. Researchers explore the potential of Large Language Models (LLMs) to transform these explanations into human-readable narrative formats aligned with natural communication. The study addresses two key research questions: can LLMs reliably convert traditional explanations into high-quality narratives? and how can we effectively evaluate the quality of narrative explanations? To answer these questions, the authors introduce Explingo, comprising two LLM-based subsystems: Narrator and Grader. The Narrator takes in ML explanations and transforms them into natural-language descriptions. The Grader scores these narratives on metrics including accuracy, completeness, fluency, and conciseness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can help us make decisions by explaining their predictions. But these explanations are hard for humans to understand. Researchers want to know if large language models can turn these explanations into stories that people can read easily. They also want to figure out how to measure the quality of these story explanations. To do this, they created a system called Explingo with two parts: Narrator and Grader. The Narrator takes in the model’s explanation and turns it into a natural language description. The Grader checks how good this narrative is by looking at things like accuracy, completeness, flow, and shortness. |
Keywords
» Artificial intelligence » Machine learning
