Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts

arXiv:2404.02022v1 Announce Type: new
Abstract: In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints including model sizes and computing resources, the length of context is often limited, and it becomes challenging to empower the model to cover overlong contexts while answering questions from open domains. This paper proposes a general and convenient method to covering longer contexts in Open-Domain Question-Answering tasks. It leverages a small encoder …

abstract arxiv computing computing resources constraints context cs.cl domain improving language language models large language large language models question resources retrieval retrieval augmented generation type