Detecting text generated by large language models (LLMs) is of great recent interest.

Such data can take numerous forms, for instance points, direction vectors, translations, or rotations, but to date there is no single architecture that can be applied to such a wide variety of geometric types while respecting their symmetries. In this paper we introduce the Geometric Algebra Transformer (GA Tr), a general-purpose architecture for geometric data.

Neural Information Processing Systems http://nips.cc/

Asuccessful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of autoformalization seemed elusive for a long time, we show large language models provide new prospects towards this goal.