Synthetic-to-Real Data Ratio Effects on Transformer Generalization in Tabular Domains

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the impact of varying the synthetic-to-real data ratio during fine-tuning on the generalization performance of TFMs across heterogeneous tabular datasets, as measured by average accuracy on. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the impact of varying the synthetic-to-real data ratio during fine-tuning on the generalization performance of TFMs across heterogeneous tabular datasets, as measured by average accuracy on TabBench and TabFair benchmarks?

Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.