Speaker：Dr. Wen-Yu Hua (Apple Machine Learning Engineer)
Topic：LACoS-BLOOM
Low-rank Adaptation with Contrastive objective on 8-bit Siamese-BLOOM
Speaker：Dr. Wen-Yu Hua (Apple Machine Learning Engineer)
Date Time：Thu. Dec 7, 2023, 12:10 - 13:00
Place： 4F-427, Assembly Building I
Online Seminars- Google Meet
Abstract
Text embeddings are useful features for several NLP applications, such as sentence similarity, text clustering, and semantic search. In this paper, we present a Low-rank Adaptation with a Contrastive objective on top of 8-bit Siamese-BLOOM, a multilingual large lan- guage model optimized to produce semantically meaningful word embeddings. The innovation is threefold. First, we cast BLOOM weights to 8-bit values.
Second, we fine-tune BLOOM with a scalable adapter (LoRA) and 8-bit Adam optimizer for sentence similarity classification. Third, we apply a Siamese architecture on BLOOM model with a contrastive objective to ease the multi-lingual labeled data scarcity. The experiment results show the quality of learned embeddings from LACoS-BLOOM is proportional to the number of model parameters and the amount of unlabeled training data. With the parameter efficient fine-tuning design, we are able to run BLOOM 7.1 billion parameters end-to-end on a single GPU ma- chine with 32GB memory. Compared to previous solution
Sentence- BERT, we achieve significant improvement on both English and multi-lingual STS tasks.