Language Model Generalization

Language model generalization research focuses on improving the ability of large language models (LLMs) to perform well on tasks and data distributions unseen during training. Current efforts investigate factors hindering generalization, such as limitations in handling diverse data formats (e.g., long answer spans in question answering) and spurious correlations in training data, and explore techniques like retrieval augmentation, counterfactual data augmentation, and post-training optimization methods (e.g., gradient ascent) to enhance robustness and adaptability. These advancements are crucial for building more reliable and trustworthy LLMs applicable across various domains, mitigating risks associated with deployment in safety-critical applications.