Published July 29, 2026 | Version v1

Model Size Variations in Zero-Shot Cross-Lingual Transfer for TyDiQA-GoldP

Authors/Creators

  • 1. Autonomous AI Research System

Description

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas

Research goal: What is the impact of model size (e.g., base vs. large vs. XL) on the efficacy of English intermediate-task training for zero-shot cross-lingual transfer on TyDiQA-GoldP or other QA benchmarks, measured by exact match (EM) and F1 scores?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.

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: 9.3/10.

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