SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs

arXiv:2402.10979v1 Announce Type: new
Abstract: Large language models hold significant potential for integrating various data types, such as text documents and database records, for advanced analytics. However, blending text and numerical data presents substantial challenges. LLMs need to process and cross-reference entities and numbers, handle data inconsistencies and redundancies, and develop planning capabilities such as building a working memory for managing complex data queries. In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the …

abstract advanced advanced analytics analytics arxiv challenges cs.ai cs.cl data database documents fusion information language language models large language large language models llms numbers numerical process records reference text type types