MORTAR: Metamorphic Multi-turn Testing for LLM-based Dialogue Systems

• URL: http://arxiv.org/abs/2412.15557v1
• Date: Fri, 20 Dec 2024 04:31:03 GMT
• Title: MORTAR: Metamorphic Multi-turn Testing for LLM-based Dialogue Systems
• Authors: Guoxiang Guo, Aldeida Aleti, Neelofar Neelofar, Chakkrit Tantithamthavorn,
• Abstract summary: We propose MORTAR, a MetamORphic multi-TuRn diAlogue testing appRoach. MorTAR automates the generation of follow-up question-answer (QA) dialogue test cases. It detects bugs of multi-turn dialogue systems in a low-cost manner.
• Score: 7.7097144952707435
• License:
• Abstract: With the widespread application of LLM-based dialogue systems in daily life, quality assurance has become more important than ever. Recent research has successfully introduced methods to identify unexpected behaviour in single-turn scenarios. However, multi-turn dialogue testing remains underexplored, with the Oracle problem in multi-turn testing posing a persistent challenge for dialogue system developers and researchers. In this paper, we propose MORTAR, a MetamORphic multi-TuRn diAlogue testing appRoach, which mitigates the test oracle problem in the assessment of LLM-based dialogue systems. MORTAR automates the generation of follow-up question-answer (QA) dialogue test cases with multiple dialogue-level perturbations and metamorphic relations. MORTAR employs a novel knowledge graph-based dialogue information model which effectively generates perturbed dialogue test datasets and detects bugs of multi-turn dialogue systems in a low-cost manner. The proposed approach does not require an LLM as a judge, eliminating potential of any biases in the evaluation step. According to the experiment results on multiple LLM-based dialogue systems and comparisons with single-turn metamorphic testing approaches, MORTAR explores more unique bugs in LLM-based dialogue systems, especially for severe bugs that MORTAR detects up to four times more unique bugs than the most effective existing metamorphic testing approach.

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