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

Abstract

Abstract: 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 Question

How does inference-time scaling affect the cross-lingual consistency of factual knowledge in multilingual PLMs when evaluated using the RankC metric?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance