Impact of Intermediate Task Difficulty on Zero-Shot Cross-Lingual Transfer Performance in Multilingual LLMs

Multilingual BERT (mBERT), a language model pre-trained on large multilingual corpora, has impressive zero-shot cross-lingual transfer capabilities and performs surprisingly well on zero-shot POS tagging and Named Entity Recognition (NER), as well as on cross-lingual model transfer. At present, the mainstream methods to solve the cross-lingual downstream tasks are always using the last transformer layer's output of mBERT as the representation of linguistic information. In this work, we explore the complementary property of lower layers to the last transformer layer of mBERT. A feature aggregat

Research goal: How does the choice of intermediate task difficulty (easy vs. hard) in English affect zero-shot cross-lingual transfer performance on the XTREME-R benchmark when using multilingual LLMs like mBERT or XLM-R, measured by accuracy improvements across languages?

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