Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it is important to develop Long Context Language Models (LCLMs) that can process and analyze extensive inputs in an effective and efficient way. In this paper, we present a comprehensive survey on recent advances in long-context modeling for large language models. Our survey is structured around three key aspects: how to obtain effective and efficient LCLMs, how to train and deploy LCLMs efficiently, and how to evaluate and analyze LCLMs comprehensively. For the first aspect, we discuss data strategies, architectural designs, and workflow approaches oriented with long context processing. For the second aspect, we provide a detailed examination of the infrastructure required for LCLM training and inference. For the third aspect, we present evaluation paradigms for long-context comprehension and long-form generation, as well as behavioral analysis and mechanism interpretability of LCLMs. Beyond these three key aspects, we thoroughly explore the diverse application scenarios where existing LCLMs have been deployed and outline promising future development directions. This survey provides an up-to-date review of the literature on long-context LLMs, which we wish to serve as a valuable resource for both researchers and engineers. An associated GitHub repository collecting the latest papers and repos is available at: \href{this https URL}{\color[RGB]{175,36,67}{LCLM-Horizon}}.
View on arXiv@article{liu2025_2503.17407,
title={ A Comprehensive Survey on Long Context Language Modeling },
author={ Jiaheng Liu and Dawei Zhu and Zhiqi Bai and Yancheng He and Huanxuan Liao and Haoran Que and Zekun Wang and Chenchen Zhang and Ge Zhang and Jiebin Zhang and Yuanxing Zhang and Zhuo Chen and Hangyu Guo and Shilong Li and Ziqiang Liu and Yong Shan and Yifan Song and Jiayi Tian and Wenhao Wu and Zhejian Zhou and Ruijie Zhu and Junlan Feng and Yang Gao and Shizhu He and Zhoujun Li and Tianyu Liu and Fanyu Meng and Wenbo Su and Yingshui Tan and Zili Wang and Jian Yang and Wei Ye and Bo Zheng and Wangchunshu Zhou and Wenhao Huang and Sujian Li and Zhaoxiang Zhang },
journal={arXiv preprint arXiv:2503.17407},
year={ 2025 }
}