Large Language Models as General Pattern Machines
We observe that pre-trained large language models (LLMs) are capable of
autoregressively completing complex token sequences -- from arbitrary ones
procedurally generated by probabilistic context-free grammars (PCFG), to more
rich spatial patterns found in the Abstract Reasoning Corpus (ARC), a general
AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern
completion proficiency can be partially retained even when the sequences are
expressed using tokens randomly sampled from the vocabulary. These results
suggest that without any additional training, LLMs can serve as general
sequence modelers, driven by in-context learning. In this work, we investigate
how these zero-shot capabilities may be applied to problems in robotics -- from
extrapolating sequences of numbers that represent states over time to complete
simple motions, to least-to-most prompting of reward-conditioned trajectories
that can discover and represent closed-loop policies (e.g., a stabilizing
controller for CartPole). While difficult to deploy today for real systems due
to latency, context size limitations, and compute costs, the approach of using
LLMs to drive low-level control may provide an exciting glimpse into how the
patterns among words could be transferred to actions.
64acb42a5c6fc404f0d526a6
Suvir Mirchandani
64acb42a5c6fc404f0d526a7
Fei Xia
claimed_verified
2024-01-23T08:10:49.554Z
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/avatars/8bfe3bf63f0609b3b6b7d7c5d7f6b3e9.svg
Fei Xia
user
fxia22
64acb42a5c6fc404f0d526a8
Pete Florence
64acb42a5c6fc404f0d526a9
Brian Ichter
64acb42a5c6fc404f0d526aa
Danny Driess
64acb42a5c6fc404f0d526ab
Montserrat Gonzalez Arenas
64acb42a5c6fc404f0d526ac
Kanishka Rao
64acb42a5c6fc404f0d526ad
Dorsa Sadigh
64acb42a5c6fc404f0d526ae
Andy Zeng
claimed_verified
2023-07-12T07:29:45.271Z
62d5f9d705461e9459d20b91
/avatars/120917da2d8587674505163fb960e27d.svg
Andy Zeng
user
andyzeng
Large Language Models as General Pattern Machines
We observe that pre-trained large language models (LLMs) are capable of
autoregressively completing complex token sequences -- from arbitrary ones
procedurally generated by probabilistic context-free grammars (PCFG), to more
rich spatial patterns found in the Abstract Reasoning Corpus (ARC), a general
AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern
completion proficiency can be partially retained even when the sequences are
expressed using tokens randomly sampled from the vocabulary. These results
suggest that without any additional training, LLMs can serve as general
sequence modelers, driven by in-context learning. In this work, we investigate
how these zero-shot capabilities may be applied to problems in robotics -- from
extrapolating sequences of numbers that represent states over time to complete
simple motions, to least-to-most prompting of reward-conditioned trajectories
that can discover and represent closed-loop policies (e.g., a stabilizing
controller for CartPole). While difficult to deploy today for real systems due
to latency, context size limitations, and compute costs, the approach of using
LLMs to drive low-level control may provide an exciting glimpse into how the
patterns among words could be transferred to actions.
Large language models demonstrate zero-shot sequence completion and can extrapolate actions in robotics tasks, offering a potential transfer of word patterns to control policies.
pre-trained large language models autoregressive completion probabilistic context-free grammars Abstract Reasoning Corpus in-context learning zero-shot capabilities sequence modelers extrapoltation of sequences reward-conditioned trajectories closed-loop policies stabilizing controller CartPole