arxiv Preprint – Retrieval meets Long Context Large Language Models

In this episode we discuss Retrieval meets Long Context Large Language Models
by Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro. This paper compares two methods for handling long context in large language models (LLMs): retrieval-augmentation and extending the context window. The study finds that LLMs with a 4K context window using retrieval-augmentation achieve similar performance to LLMs with a 16K context window through positional interpolation, while requiring less computation. Moreover, the authors demonstrate that retrieval significantly improves LLM performance regardless of the context window size.