This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of varying the number of negative samples in adversarial contrastive learning on inference throughput for cross-lingual rumor detection in TyDi QA subsets. Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.Research goal: What is the impact of varying the number of negative samples in adversarial contrastive learning on inference throughput for cross-lingual rumor detection in TyDi QA subsets?Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.