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 language model that effectively encodes contexts, and the encoding applies cross-attention with origin inputs. With our method, the origin language models can cover several times longer contexts while keeping the computing requirements close to the baseline. Our experiments demonstrate that after fine-tuning, there is improved performance across two held-in datasets, four held-out datasets, and also in two In Context Learning settings.

翻译：在大语言模型时代，应用检索增强生成等技术能够更好地解决开放域问答问题。由于模型规模和计算资源等限制，上下文长度通常有限，在回答开放域问题时难以让模型覆盖过长的上下文。本文提出了一种通用且便捷的方法，用于覆盖开放域问答任务中的更长上下文。该方法利用一个小型编码器语言模型对上下文进行有效编码，并将编码结果与原始输入进行交叉注意力计算。采用我们的方法后，原始语言模型能够覆盖数倍于原有长度的上下文，同时计算需求与基线方法基本持平。实验表明，微调后该方法在两个保留数据集、四个未见数据集以及两种上下文学习设置中均实现了性能提升。