arxiv preprint - Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning

In this episode, we discuss Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning by Trapoom Ukarapol, Zhicheng Lee, Amy Xin. The paper investigates enhancing smaller language models, like MiniCPM, through improved text embeddings via contrastive fine-tuning on the NLI dataset. Results indicate that this fine-tuning significantly improves performance across multiple benchmarks, with MiniCPM showing a notable 56.33% performance gain. The study's code is available at https://github.com/trapoom555/Language-Model-STS-CFT.