arxiv preprint – Large Language Models as General Pattern Machines

In this episode we discuss Large Language Models as General Pattern Machines
by Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, Andy Zeng. The paper discusses the capabilities of pre-trained large language models (LLMs) in completing complex token sequences. The study shows that LLMs can effectively complete sequences generated by probabilistic context-free grammars and intricate spatial patterns found in Abstract Reasoning Corpus. These capabilities suggest that LLMs can serve as general sequence modelers without any additional training, which can be applied to robotics, such as extrapolating sequences of numbers representing states over time and prompting reward-conditioned trajectories.