LLMs as General Pattern Machines
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Demonstrates LLMs serve as general sequence modelers without additional training.
Zero-shot sequence modeling: Shows LLMs can complete arbitrary symbolic sequences, not just language - they're general pattern completers driven by in-context learning.
Word-to-action transfer: Applies pattern-completion to robotics, transferring abstract sequence patterns from text directly into robot action sequences.
Robotics without robot data: Achieves meaningful robot control without any training on robot data - purely through language model pattern-matching.
Conceptual framing: Influential perspective paper reframing LLMs as general compression/pattern machines rather than just language models.
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