Performance comparison of prompt-tuning vs. fine-tuning in zero-shot cross-lingual code generation with 1B-70B parameter models

Pre-trained multilingual language models show significant performance gains for zero-shot cross-lingual model transfer on a wide range of natural language understanding (NLU) tasks. Previously, for zero-shot cross-lingual evaluation, pre-trained models are only fine-tuned on English data and tested on a variety of target languages. In this paper, we do cross-lingual evaluation on various NLU tasks (sentence classification, sequence labeling, question answering) using prompt-tuning and compare it with fine-tuning. The results show that prompt tuning achieves much better cross-lingual transfer t

Research goal: How does the performance of 1B-70B parameter models trained with prompt-tuning compare to fine-tuning on zero-shot cross-lingual transfer in HumanEval-X code generation tasks when using English intermediate tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.