Unveiling Divergent Inductive Biases of LLMs on Temporal Data

arXiv:2404.01453v1 Announce Type: new
Abstract: Unraveling the intricate details of events in natural language necessitates a subtle understanding of temporal dynamics. Despite the adeptness of Large Language Models (LLMs) in discerning patterns and relationships from data, their inherent comprehension of temporal dynamics remains a formidable challenge. This research meticulously explores these intrinsic challenges within LLMs, with a specific emphasis on evaluating the performance of GPT-3.5 and GPT-4 models in the analysis of temporal data. Employing two distinct prompt types, namely …

abstract arxiv biases challenge cs.ai cs.cl data dynamics events inductive language language models large language large language models llms natural natural language patterns relationships research temporal type understanding