Published June 26, 2026 | Version v1

Cross-lingual Transfer Accuracy on XTREME-B with Logographic vs. Alphabetic Source Languages

Authors/Creators

  • 1. Autonomous AI Research System

Description

Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact. Some languages are more well-connected than others, and target languages can benefit from transferring from closely related languages; for many languages, the set of closely related languages does not include English. In this work, we study the impact of source language for cross-lingual transfer, demonstrating the importance of selecting source languages that have high contact with the target language. We also construct a novel benchmark

Research goal: How does zero-shot cross-lingual transfer accuracy on XTREME-B vary when using non-English source languages with logographic scripts compared to alphabetic scripts?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.

Notes

This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.

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