Low-Resource Adaptation of Large Language Models for Regional Dialects

Fri, 01 Nov 2024 00:00:00 +0000

Abstract

Large language models perform unevenly across regional dialects that are underrepresented in web-scale training corpora. We propose a parameter-efficient adaptation method that fine-tunes a small set of dialect-specific adapter layers using as few as 5,000 labeled examples, substantially closing the performance gap on downstream tasks including sentiment analysis, named entity recognition, and machine translation for four regional dialect groups.

Low-Resource on Abhiraj Bibhar

Low-Resource Adaptation of Large Language Models for Regional Dialects

Abstract