GLARE: A Natural Language Interface for Querying Global Explanations

2026-06-18 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

GLARE is an LLM-based interactive interface designed to provide natural language access to global explanations for black-box image classifiers. This system addresses the complexity of global explanations, which often hinder practical exploration, by allowing users to ask targeted questions. Its core Large Language Model mediates by translating natural language queries into structured SQL queries over local explanation data. The interface then outputs statistics-augmented natural language responses, supporting local explanations, and intent-aligned visualizations. Evaluation results demonstrate that LLM-mediated querying substantially improves the accessibility and usability of global explanations, advancing human-centered Explainable AI (XAI).

Key takeaway

For Machine Learning Engineers seeking to enhance the interpretability of black-box vision models, you should consider integrating LLM-based interactive interfaces like GLARE. This approach allows users to query complex global explanations using natural language, significantly improving accessibility and usability. By translating natural language into structured SQL queries, such systems enable dynamic exploration of model behaviors, moving beyond static explanation artifacts.

Key insights

LLM-based interfaces can transform complex global explanations into accessible, interactive natural language queries for black-box models.

Principles

• Global explanations are crucial but often too complex for practical use.
• Users prefer targeted answers over static explanation artifacts.
• LLMs can mediate between natural language and structured data queries.

Method

An LLM translates natural language questions into SQL queries over local explanation data, then generates statistics-augmented natural language responses with visualizations.

In practice

• Implement LLM-mediated interfaces for XAI.
• Query explanation data using SQL for flexible aggregation.

Topics

• GLARE
• LLM Interfaces
• Global Explanations
• Explainable AI
• Black-box Models
• Image Classifiers
• SQL Queries

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.