Behavioral testing in NLP evaluates system capabilities by analyzing input-output behavior. However, current tests for Machine Translation are limited and manually created. To overcome this, our proposal suggests using Large Language Models (LLMs) to generate diverse source sentences for testing MT model behavior in various scenarios. Verification ensures expected performance.
Automating Behavioral Testing in Machine Translation: Enhance Your Company with AI
Behavioral testing in NLP allows for a detailed evaluation of systems by analyzing their linguistic capabilities through input-output behavior analysis. However, current behavioral testing in Machine Translation (MT) is limited to handcrafted tests that cover only a small range of capabilities and languages.
To overcome this limitation, we propose using Large Language Models (LLMs) to generate a diverse set of source sentences specifically designed to test the behavior of MT models in various situations. By doing so, we can verify whether the MT model exhibits the expected behavior.
If you want to evolve your company with AI and stay competitive, automating behavioral testing in Machine Translation can be a valuable solution.
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