Large language models (LLMs) exhibit a wide range of promising capabilities
-- from step-by-step planning to commonsense reasoning -- that may provide
utility for robots, but remain prone to confidently hallucinated predictions.
In this work, we present KnowNo, which is a framework for measuring and
aligning the uncertainty of LLM-based planners such that they know when they
don't know and ask for help when needed. KnowNo builds on the theory of
conformal prediction to provide statistical guarantees on task completion while
minimizing human help in complex multi-step planning settings. Experiments
across a variety of simulated and real robot setups that involve tasks with
different modes of ambiguity (e.g., from spatial to numeric uncertainties, from
human preferences to Winograd schemas) show that KnowNo performs favorably over
modern baselines (which may involve ensembles or extensive prompt tuning) in
terms of improving efficiency and autonomy, while providing formal assurances.
KnowNo can be used with LLMs out of the box without model-finetuning, and
suggests a promising lightweight approach to modeling uncertainty that can
complement and scale with the growing capabilities of foundation models.
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该论文介绍了一种针对大型语言模型的不确定性测量和调整的框架，使用收敛性预测理论提供任务完成的统计保证，同时在复杂的多步计划设置中最小化人类干预，为机器人增加了有效性和自主性，并建议一种轻量级的不确定性建模方法，以补充和扩展基础模型的不断增长的能力。