title: A Comprehensive Survey on Long Context Language Modeling
summary: 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:
  https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling{\color[RGB]{175,36,67}{LCLM-Horizon}}.
paper_authors._id: 67e204089e7142f579abb84a
paper_authors.name: Jiaheng Liu
paper_authors._id: 67e204089e7142f579abb84b
paper_authors.name: Dawei Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:47.031Z
paper_authors._id: 64d2fce8129a210e569e0c76
paper_authors.avatarUrl: /avatars/a79a832dc3a46ece1b9e542369fc4888.svg
paper_authors.fullname: Dawei Zhu
paper_authors.type: user
paper_authors.user: dwzhu
paper_authors._id: 67e204089e7142f579abb84c
paper_authors.name: Zhiqi Bai
paper_authors._id: 67e204089e7142f579abb84d
paper_authors.name: Yancheng He
paper_authors._id: 67e204089e7142f579abb84e
paper_authors.name: Huanxuan Liao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:43.983Z
paper_authors._id: 634900f08146350b3a4b6710
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1665728713794-noauth.jpeg
paper_authors.fullname: Xnhyacinth
paper_authors.type: user
paper_authors.user: Xnhyacinth
paper_authors._id: 67e204089e7142f579abb84f
paper_authors.name: Haoran Que
paper_authors._id: 67e204089e7142f579abb850
paper_authors.name: Zekun Wang
paper_authors._id: 67e204089e7142f579abb851
paper_authors.name: Chenchen Zhang
paper_authors._id: 67e204089e7142f579abb852
paper_authors.name: Ge Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T08:13:26.809Z
paper_authors._id: 638efcf4c67af472d316d424
paper_authors.avatarUrl: /avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
paper_authors.fullname: Ge Zhang
paper_authors.type: user
paper_authors.user: zhangysk
paper_authors._id: 67e204089e7142f579abb853
paper_authors.name: Jiebin Zhang
paper_authors._id: 67e204089e7142f579abb854
paper_authors.name: Yuanxing Zhang
paper_authors._id: 67e204089e7142f579abb855
paper_authors.name: Zhuo Chen
paper_authors._id: 67e204089e7142f579abb856
paper_authors.name: Hangyu Guo
paper_authors._id: 67e204089e7142f579abb857
paper_authors.name: Shilong Li
paper_authors._id: 67e204089e7142f579abb858
paper_authors.name: Ziqiang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:52.088Z
paper_authors._id: 61b9b5f9fd429dff1ed3bd58
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61b9b5f9fd429dff1ed3bd58/GrfeNU4K1zts-gmQdGbVE.jpeg
paper_authors.fullname: Ziqiang Liu
paper_authors.type: user
paper_authors.user: icoderzqliu
paper_authors._id: 67e204089e7142f579abb859
paper_authors.name: Yong Shan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:49.963Z
paper_authors._id: 64b8cc7ebe76d2ff0703bfb3
paper_authors.avatarUrl: /avatars/f1c7ff17fd923f1460d362333d9fbfe3.svg
paper_authors.fullname: sh
paper_authors.type: user
paper_authors.user: yo37
paper_authors._id: 67e204089e7142f579abb85a
paper_authors.name: Yifan Song
paper_authors._id: 67e204089e7142f579abb85b
paper_authors.name: Jiayi Tian
paper_authors._id: 67e204089e7142f579abb85c
paper_authors.name: Wenhao Wu
paper_authors._id: 67e204089e7142f579abb85d
paper_authors.name: Zhejian Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:54.197Z
paper_authors._id: 61f650d9bf54777a520c599d
paper_authors.avatarUrl: /avatars/9e7b4fc709835c0934bf2671298c9b33.svg
paper_authors.fullname: Jeremiah Zhou
paper_authors.type: user
paper_authors.user: JeremiahZ
paper_authors._id: 67e204089e7142f579abb85e
paper_authors.name: Ruijie Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:20:56.102Z
paper_authors._id: 63ff09f24852102d4871c19c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63ff09f24852102d4871c19c/lyE3xemtZss3qebK5sEXw.png
paper_authors.fullname: Rui-Jie Zhu
paper_authors.type: user
paper_authors.user: ridger
paper_authors._id: 67e204089e7142f579abb85f
paper_authors.name: Junlan Feng
paper_authors._id: 67e204089e7142f579abb860
paper_authors.name: Yang Gao
paper_authors._id: 67e204089e7142f579abb861
paper_authors.name: Shizhu He
paper_authors._id: 67e204089e7142f579abb862
paper_authors.name: Zhoujun Li
paper_authors._id: 67e204089e7142f579abb863
paper_authors.name: Tianyu Liu
paper_authors._id: 67e204089e7142f579abb864
paper_authors.name: Fanyu Meng
paper_authors._id: 67e204089e7142f579abb865
paper_authors.name: Wenbo Su
paper_authors._id: 67e204089e7142f579abb866
paper_authors.name: Yingshui Tan
paper_authors._id: 67e204089e7142f579abb867
paper_authors.name: Zili Wang
paper_authors._id: 67e204089e7142f579abb868
paper_authors.name: Jian Yang
paper_authors._id: 67e204089e7142f579abb869
paper_authors.name: Wei Ye
paper_authors._id: 67e204089e7142f579abb86a
paper_authors.name: Bo Zheng
paper_authors._id: 67e204089e7142f579abb86b
paper_authors.name: Wangchunshu Zhou
paper_authors._id: 67e204089e7142f579abb86c
paper_authors.name: Wenhao Huang
paper_authors._id: 67e204089e7142f579abb86d
paper_authors.name: Sujian Li
paper_authors._id: 67e204089e7142f579abb86e
paper_authors.name: Zhaoxiang Zhang
paper_title: A Comprehensive Survey on Long Context Language Modeling
paper_summary: 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:
  https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling{\color[RGB]{175,36,67}{LCLM-Horizon}}.
paper_ai_summary: This survey examines recent advancements in long-context language modeling, focusing on methods for obtaining, training, deploying, and evaluating models capable of processing extensive textual inputs.
paper_ai_keywords: Long Context Language Models (LCLMs) long context processing data strategies architectural designs workflow approaches infrastructure long-context comprehension long-form generation behavioral analysis mechanism interpretability