DetAIL : A Tool to Automatically Detect and Analyze Drift In Language

TL;DR

This paper introduces DetAIL, a tool for automatically detecting and analyzing data drift in language models, providing explanations to understand why drift occurs, aiming to improve trustworthiness of machine learning systems.

Contribution

The work presents a novel tool that measures data drift in language models and offers explanations at sentence and dataset levels, enabling adaptive re-training.

Findings

Effective drift detection in language data

Generation of explanations for drift causes

Supports adaptive model updating

Abstract

Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software. Traditional software is made dependable by following rigorous practice like static analysis, testing, debugging, verifying, and repairing throughout the development and maintenance life-cycle. Similarly for machine learning systems, we need to keep these models up to date so that their performance is not compromised. For this, current systems rely on scheduled re-training of these models as new data kicks in. In this work, we propose to measure the data drift that takes place when new data kicks in so that one can adaptively re-train the models whenever re-training is actually required irrespective of schedules. In addition to that, we generate various explanations at sentence level and dataset level to capture why a given payload text has drifted.