AI RESEARCH
Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives
arXiv CS.CL
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ArXi:2510.18173v2 Announce Type: replace Large language model (LLM) approaches to tabular summarization rely on extensive prompt engineering, decomposition pipelines, or entity-level intermediate representations to achieve strong performance. While effective, these strategies are computationally expensive and offer limited insight into how well models maintain state over long, evolving narratives. We