1 citations · 1 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models
Ken E. Friedl, Abbas Goher Khan, Soumya Ranjan Sahoo +3
The assessment of advanced generative large language models (LLMs) poses a significant challenge, given their heightened complexity in recent developments. Furthermore, evaluating…
CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
Md Rashad Al Hasan Rony, Christian Suess, Sinchana Ramakanth Bhat +6
Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. However…