Bootstrapping Cognitive Agents with a Large Language Model
Feiyu Zhu Reid Simmons
Large language models contain noisy general knowledge of the world, yet are
hard to train or fine-tune. On the other hand cognitive architectures have
excellent interpretability and are flexible to update but require a lot of
manual work to instantiate. In this work, we combine the best of both worlds:
bootstrapping a cognitive-based model with the noisy knowledge encoded in large
language models. Through an embodied agent doing kitchen tasks, we show that
our proposed framework yields better efficiency compared to an agent based
entirely on large language models. Our experiments indicate that large language
models are a good source of information for cognitive architectures, and the
cognitive architecture in turn can verify and update the knowledge of large
language models to a specific domain.