Exploring Length Generalization in Large Language Models

This paper explores the length generalization capabilities of transformer-based language models and identifies deficiencies in naively finetuning on length generalization tasks. However, combining pretrained large language models' in-context learning abilities with scratchpad prompting results in a significant improvement in length generalization.

Businesses can leverage this research to improve natural language processing tasks, such as summarizing large texts or solving complex quantitative problems. This approach can enable the development of more robust AI models that can generalize to longer and more complex inputs.