Robust Fine Tuning

Robust fine-tuning focuses on adapting pre-trained large language models (LLMs) and vision-language models (VLMs) to downstream tasks while preserving their robustness to out-of-distribution (OOD) data and mitigating issues like hallucinations and bias. Current research emphasizes methods like adapter modules, selective weight updates, and uncertainty-sensitive training to improve OOD generalization and calibration, often employing techniques such as contrastive loss, projection methods, and data augmentation. These advancements are crucial for deploying reliable and fair AI systems in real-world applications, addressing limitations of standard fine-tuning approaches that can lead to performance degradation and bias amplification in unseen scenarios.