Papers by Ritam Upadhyay
Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language model (LLM) approaches to tabular summarization rely on prompt engineering, decomposition pipelines, or entity-level intermediate representations to achieve strong performance. |
| Approach: | They propose a diagnostic benchmark for long-context tabular summarization using decomposition pipelines and entity-level intermediate representations. |
| Outcome: | The proposed benchmark improves accuracy and numerical fidelity, but lacks local arithmetic. |