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Automated robustness testing for LLM-based natural language processing software
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AI
DOI：10.1016/j.eswa.2025.130642.png)
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Abstract
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• Propose AORTA, an automated robustness testing framework for LLM-based NLP software. • Develop ABS, an efficient testing method using adaptive beam search and backtracking. • Achieve an average test validity of 86.1. • Reduce test cost by up to 3442s and 219 queries per case over the strongest baseline.
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