Unlocking the Secrets of AI Explanations
Machine-Learning Models Can Make Mistakes
Machine-learning models can make mistakes and be difficult to use, so scientists have developed explanation methods to help users understand when and how they should trust a model’s predictions. These explanations are often complex, perhaps containing information about hundreds of model features. And they are sometimes presented as multifaceted visualizations that can be difficult for users who lack machine-learning expertise to fully comprehend.
Introducing EXPLINGO
To help people make sense of AI explanations, MIT researchers have developed a two-part system called EXPLINGO. This system converts a machine-learning explanation into a paragraph of human-readable text and then automatically evaluates the quality of the narrative, so an end-user knows whether to trust it.
How EXPLINGO Works
EXPLINGO is divided into two parts: NARRATOR and GRADER. NARRATOR uses a large language model (LLM) to create narrative descriptions of SHAP explanations that meet user preferences. By initially feeding NARRATOR three to five written examples of narrative explanations, the LLM will mimic that style when generating text. This allows NARRATOR to be easily customized for new use cases by showing it a different set of manually written examples.
GRADER uses an LLM to rate the narrative on four metrics: conciseness, accuracy, completeness, and fluency. GRADER automatically prompts the LLM with the text from NARRATOR and the SHAP explanation it describes. Users can also customize GRADER to give different weights to each metric.
Benefits of EXPLINGO
EXPLINGO has several benefits, including:
* It makes it easier for users to understand AI explanations by providing a narrative that is easy to read and comprehend.
* It allows users to customize the style of the explanation to their preferences.
* It evaluates the quality of the narrative, so users know whether to trust it.
* It can be easily expanded to include rationalization, which can help users understand the reasoning behind the model’s predictions.
Future Developments
The researchers plan to build upon this technique by enabling users to ask a model follow-up questions about an explanation. This would allow users to have a more in-depth conversation with the model, which could lead to better decision-making.
Conclusion
EXPLINGO is a powerful tool that can help users understand AI explanations and make better decisions. By providing a narrative that is easy to read and comprehend, EXPLINGO can help bridge the gap between humans and machines. As the technology continues to evolve, we can expect to see even more innovative applications of machine learning in various fields.
Frequently Asked Questions
Q: What is EXPLINGO?
A: EXPLINGO is a two-part system that converts a machine-learning explanation into a paragraph of human-readable text and then evaluates the quality of the narrative.
Q: How does NARRATOR work?
A: NARRATOR uses a large language model (LLM) to create narrative descriptions of SHAP explanations that meet user preferences.
Q: What is GRADER?
A: GRADER uses an LLM to rate the narrative on four metrics: conciseness, accuracy, completeness, and fluency.
Q: Can I customize EXPLINGO?
A: Yes, you can customize NARRATOR by providing three to five written examples of narrative explanations, and you can customize GRADER by giving different weights to each metric.
