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cs.CL2026
LoCar: Localization-Aware Evaluation of In-Vehicle Assistants through Fine-Grained Sociolinguistic Control
Seogyeong Jeong, Kiwoong Park, Seyoung Song +4
While Large Language Models (LLMs) are increasingly integrated into in-vehicle conversational systems, identifying the optimal model remains challenging due to the lack of domain-s…
cs.CL2025
Benchmarking Contextual Understanding for In-Car Conversational Systems
Philipp Habicht, Lev Sorokin, Abdullah Saydemir +2
In-Car Conversational Question Answering (ConvQA) systems significantly enhance user experience by enabling seamless voice interactions. However, assessing their accuracy and relia…
cs.CL2025
Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models
Rafael Giebisch, Ken E. Friedl, Lev Sorokin +1
In-car conversational systems bring the promise to improve the in-vehicle user experience. Modern conversational systems are based on Large Language Models (LLMs), which makes them…