Can only LLMs do Reasoning?: Potential of Small Language Models in Task
  Planning
Gawon Choi Hyemin Ahn
In robotics, the use of Large Language Models (LLMs) is becoming prevalent,
especially for understanding human commands. In particular, LLMs are utilized
as domain-agnostic task planners for high-level human commands. LLMs are
capable of Chain-of-Thought (CoT) reasoning, and this allows LLMs to be task
planners. However, we need to consider that modern robots still struggle to
perform complex actions, and the domains where robots can be deployed are
limited in practice. This leads us to pose a question: If small LMs can be
trained to reason in chains within a single domain, would even small LMs be
good task planners for the robots? To train smaller LMs to reason in chains, we
build `COmmand-STeps datasets' (COST) consisting of high-level commands along
with corresponding actionable low-level steps, via LLMs. We release not only
our datasets but also the prompt templates used to generate them, to allow
anyone to build datasets for their domain. We compare GPT3.5 and GPT4 with the
finetuned GPT2 for task domains, in tabletop and kitchen environments, and the
result shows that GPT2-medium is comparable to GPT3.5 for task planning in a
specific domain. Our dataset, code, and more output samples can be found in
https://github.com/Gawon-Choi/small-LMs-Task-Planning