As we conclude this extensive data collection effort, we release the entire preference dataset to the research community, fostering further advancements in AI humor generation and evaluation.

In this work, we study the performance of byte-level and subword-level autoregressive models when trained using a fixed compute budget.

Intelligent agents must be generalists, capable of quickly adapting to various tasks.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.