A Comprehensive Survey on Long Context Language Modeling

2025-03-20

AI Summary

Long-context language models (LCLMs) face persistent bottlenecks in efficiency and interpretability across modeling, training, deployment, and evaluation. Method: This work introduces the first end-to-end paradigm spanning “model construction—training & deployment—evaluation & analysis” for LCLMs. It systematically integrates sparse attention, chunking- and memory-augmented architectures, sequence compression, efficient fine-tuning, and distributed inference, while establishing a unified evaluation framework and mechanistic interpretability analysis pipeline. Contribution/Results: We publish the field’s first comprehensive survey—widely adopted as a research reference; open-source the GitHub repository *LCLM-Horizon*, continuously integrating over 100 state-of-the-art works; and advance benchmark development (e.g., long-text QA and summarization), attribution analysis techniques, and community standardization—laying the foundation for an open, collaborative LCLM research ecosystem.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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{https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling}{color[RGB]{175,36,67}{LCLM-Horizon}}.

Problem

• Efficient processing of long textual contexts
• Training and deploying long context language models
• Comprehensive evaluation of long-context model performance

Innovation

• Data strategies for long context processing
• Architectural designs for efficient LCLMs
• Evaluation paradigms for long-context comprehension