MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models

arXiv:2401.07598v2 Announce Type: replace
Abstract: Parameter Efficient Finetuning (PEFT) has emerged as a viable solution for improving the performance of Large Language Models (LLMs) without requiring massive resources and compute. Prior work on multilingual evaluation has shown that there is a large gap between the performance of LLMs on English and other languages. Further, there is also a large gap between the performance of smaller open-source models and larger LLMs. Finetuning can be an effective way to bridge this gap …

abstract arxiv compute cs.cl evaluation finetuning gap language language models large language large language models llms massive multilingual peft performance prior resources solution type work