Inference-Time Scaling and Cross-Lingual Consistency in Multilingual PLMs via RankC Metric

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does inference-time scaling affect the cross-lingual consistency of factual knowledge in multilingual PLMs when evaluated using the RankC metric. Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but large variations are observed across languages. With the ultimate goal of ensuring that users with different language backgrounds obtain consistent. 7 claims were extracted from source literature; 7 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: How does inference-time scaling affect the cross-lingual consistency of factual knowledge in multilingual PLMs when evaluated using the RankC metric?

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