Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants
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Computer Science > Computation and Language
arXiv:2609.35812 (cs)
[Submitted on 20 Sep 2026]
Title:Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants
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Abstract:In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. Ensuring their reliability is challenging due to multi-turn interactions, the absence of explicit ground truth, and strict safety constraints. Existing evaluation techniques fall short, as they target single-turn settings and fail to capture constraint handling, context retention, and safety-critical behavior across turns. We propose an automated framework for testing the multi-turn conversational capabilities of ICAs. The system is treated as a black box and evaluated via closed-loop simulation with a strategy-guided user simulator, an adversarial strategy manager, and a two-tier LLM judge assessing turn-level failures and conversation-level quality. We evaluate the approach on an industrial ICA with six LLM backends and twelve human annotators. The automated judge shows substantial agreement with humans, and strategy guidance uncovers 2.96 times more unique failure types per conversation and more than doubles the number of unique failing conversations compared to unguided simulation.
| Comments: | Accepted at the 29th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2026) |
| Subjects: | Computation and Language (cs.CL); Software Engineering (cs.SE) |
| Cite as: | arXiv:2609.35812 [cs.CL] |
| (or arXiv:2609.35812v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.35812
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants, by Vaishnav Negi and 3 other authors
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