Modal Fine Tuning

Modal fine-tuning optimizes pre-trained large language models (LLMs) for specific downstream tasks involving multiple modalities (e.g., image and text), aiming to improve efficiency and performance compared to full model retraining. Current research emphasizes parameter-efficient techniques, such as adapters and graph neural networks, to minimize computational costs while leveraging the knowledge embedded in the pre-trained models. This approach is significant because it enables the application of powerful LLMs to diverse tasks with limited data and resources, impacting fields ranging from image captioning to time series forecasting.