GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants
<|reference_start|>GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants: We tackle the challenge of building real-world multimodal assistants for complex real-world tasks. We describe the practicalities and challenges of developing and deploying GRILLBot, a leading (first and second prize winning in 2022 and 2023) system deployed in the Alexa Prize TaskBot Challenge. Building on our Open Assistant Toolkit (OAT) framework, we propose a hybrid architecture that leverages Large Language Models (LLMs) and specialised models tuned for specific subtasks requiring very low latency. OAT allows us to define when, how and which LLMs should be used in a structured and deployable manner. For knowledge-grounded question answering and live task adaptations, we show that LLM reasoning abilities over task context and world knowledge outweigh latency concerns. For dialogue state management, we implement a code generation approach and show that specialised smaller models have 84% effectiveness with 100x lower latency. Overall, we provide insights and discuss tradeoffs for deploying both traditional models and LLMs to users in complex real-world multimodal environments in the Alexa TaskBot challenge. These experiences will continue to evolve as LLMs become more capable and efficient -- fundamentally reshaping OAT and future assistant architectures.<|reference_end|>
@article{fischer2024grillbot,
    title={GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language
  Models for Adaptable Conversational Task Assistants},
    author={Sophie Fischer, Carlos Gemmell, Niklas Tecklenburg, Iain Mackie,
  Federico Rossetto and Jeffrey Dalton},
    journal={arXiv preprint arXiv:2402.07647},
    year={2024},
    doi={10.1145/3637528.3671622},
    archivePrefix={arXiv},
    eprint={2402.07647},
    primaryClass={cs.IR}
}