Towards Proactive Interactions for In-Vehicle Conversational Assistants Utilizing Large Language Models
arxiv(2024)
摘要
Research demonstrates that the proactivity of in-vehicle conversational
assistants (IVCAs) can help to reduce distractions and enhance driving safety,
better meeting users' cognitive needs. However, existing IVCAs struggle with
user intent recognition and context awareness, which leads to suboptimal
proactive interactions. Large language models (LLMs) have shown potential for
generalizing to various tasks with prompts, but their application in IVCAs and
exploration of proactive interaction remain under-explored. These raise
questions about how LLMs improve proactive interactions for IVCAs and influence
user perception. To investigate these questions systematically, we establish a
framework with five proactivity levels across two dimensions-assumption and
autonomy-for IVCAs. According to the framework, we propose a "Rewrite + ReAct +
Reflect" strategy, aiming to empower LLMs to fulfill the specific demands of
each proactivity level when interacting with users. Both feasibility and
subjective experiments are conducted. The LLM outperforms the state-of-the-art
model in success rate and achieves satisfactory results for each proactivity
level. Subjective experiments with 40 participants validate the effectiveness
of our framework and show the proactive level with strong assumptions and user
confirmation is most appropriate.
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