Xinhui Wu


2024

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SEGMENT+: Long Text Processing with Short-Context Language Models
Wei Shi | Shuang Li | Kerun Yu | Jinglei Chen | Zujie Liang | Xinhui Wu | Yuxi Qian | Feng Wei | Bo Zheng | Jiaqing Liang | Jiangjie Chen | Yanghua Xiao
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

There is a growing interest in expanding the input capacity of language models (LMs) across various domains. However, simply increasing the context window does not guarantee robust performance across diverse long-input processing tasks, such as understanding extensive documents and extracting detailed information from lengthy and noisy data. In response, we introduce Segment+, a general framework that enables LMs to handle extended inputs within limited context windows efficiently. Segment+ utilizes structured notes and a filtering module to manage information flow, resulting in a system that is both controllable and interpretable. Our extensive experiments across various model sizes, focusing on long-document question-answering and Needle-in-a-Haystack tasks, demonstrate the effectiveness of Segment+ in improving performance.