Unsupervised Fine Tuning

Unsupervised fine-tuning aims to improve pre-trained models, such as large language models (LLMs) and vision-language models (VLMs like CLIP), using unlabeled data, thereby reducing reliance on expensive and time-consuming annotation. Current research focuses on developing effective strategies for adapting these models to new tasks without labeled examples, exploring techniques like self-supervised learning, prompt engineering, and the integration of retrieval mechanisms (RAG). This area is significant because it promises to enhance the scalability and applicability of powerful pre-trained models across diverse domains, particularly in scenarios with limited labeled data.