Transfer of Structural Knowledge from Synthetic Languages

• URL: http://arxiv.org/abs/2505.15769v1
• Date: Wed, 21 May 2025 17:18:51 GMT
• Title: Transfer of Structural Knowledge from Synthetic Languages
• Authors: Mikhail Budnikov, Ivan Yamshchikov,
• Abstract summary: This work explores transfer learning from several synthetic languages to English.<n>We introduce a new synthetic language that leads to better transfer to English than the languages used in previous research.<n>We use Tiny-Cloze Benchmark to evaluate fine-tuned models in several domains demonstrating that fine-tuning on a new synthetic language allows for better performance on a variety of tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work explores transfer learning from several synthetic languages to English. We investigate the structure of the embeddings in the fine-tuned models, the information they contain, and the capabilities of the fine-tuned models on simple linguistic tasks. We also introduce a new synthetic language that leads to better transfer to English than the languages used in previous research. Finally, we introduce Tiny-Cloze Benchmark - a new synthetic benchmark for natural language understanding that is more informative for less powerful models. We use Tiny-Cloze Benchmark to evaluate fine-tuned models in several domains demonstrating that fine-tuning on a new synthetic language allows for better performance on a variety of tasks.

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