Automating Behavioral Testing in Machine Translation

TL;DR

This paper introduces a novel LLM-based framework for automated, diverse behavioral testing of machine translation systems, uncovering nuanced differences and bugs beyond traditional accuracy metrics.

Contribution

It presents a scalable, minimally supervised approach using LLMs to generate and verify test cases for evaluating MT models' linguistic behavior.

Findings

Identified differences in MT system behaviors not seen with accuracy metrics

Demonstrated the framework's ability to uncover potential bugs in MT models

Showed that pass-rates correlate with traditional metrics but reveal additional insights

Abstract

Behavioral testing in NLP allows fine-grained evaluation of systems by examining their linguistic capabilities through the analysis of input-output behavior. Unfortunately, existing work on behavioral testing in Machine Translation (MT) is currently restricted to largely handcrafted tests covering a limited range of capabilities and languages. To address this limitation, we propose to use Large Language Models (LLMs) to generate a diverse set of source sentences tailored to test the behavior of MT models in a range of situations. We can then verify whether the MT model exhibits the expected behavior through matching candidate sets that are also generated using LLMs. Our approach aims to make behavioral testing of MT systems practical while requiring only minimal human effort. In our experiments, we apply our proposed evaluation framework to assess multiple available MT systems, revealing that while in general pass-rates follow the trends observable from traditional accuracy-based metrics, our method was able to uncover several important differences and potential bugs that go unnoticed when relying only on accuracy.