Interpreting AI Predictions in Simple, Understandable Language
AI models can sometimes be tricky to use and prone to errors, which is why scientists have come up with explanation methods to help users understand when and why they should trust a model's predictions. These explanations can be complex, often containing info about hundreds of model features, and they're not always easy to comprehend, especially for those without machine-learning expertise.
To make AI explanations more understandable, researchers from MIT used large language models (LLMs) to transform complex, plot-based explanations into simpler, human-readable text. They created a two-part system called EXPLINGO: the first component, NARRATOR, uses an LLM to create narrative descriptions of machine-learning explanations that meet user preferences, and the second component, GRADER, rates the narrative on four metrics to ensure its quality and accuracy.
NARRATOR can be easily customized to meet new use cases by providing it with a different set of manually written examples. By contrast, GRADER automatically prompts the LLM with the text from NARRATOR and the SHAP explanation it describes. This system aims to enable users to have conversations with machine-learning models about the reasons for their predictions, helping them make better decisions about trusting the model.
In essence, EXPLINGO simplifies technical jargon and generates explanations in an easy-to-understand manner. It uses visual aids, model interpretability techniques, and user-centered design to make complex machine-learning insights more accessible. It also features a feedback mechanism that allows users to provide feedback on the clarity of explanations, which can help refine and improve the system over time.
- Engineers and scientists, when utilizing artificial-intelligence models, may face difficulties understanding complex explanations of the models' errors and predictions.
- ToAddress this issue, researchers from MIT developed EXPLINGO, a two-part system that simplifies AI explanations and makes them easier to comprehend, even for those without machine-learning expertise.
- The first component of EXPLINGO, NARRATOR, generates simple, narrative descriptions of machine-learning explanations tailored to user preferences, while the second component, GRADER, evaluates the narrative's quality and accuracy using four metrics.
