[2502.01481] Intrinsic Entropy of Context Length Scaling in LLMs

Computer Science > Machine Learning arXiv:2502.01481 (cs) [Submitted on 3 Feb 2025 (v1), last revised 2 Mar 2026 (this version, v4)] Title:Intrinsic Entropy of Context Length Scaling in LLMs Authors:Jingzhe Shi, Qinwei Ma, Hongyi Liu, Hang Zhao, Jeng-Neng Hwang, Lei Li View a PDF of the paper titled Intrinsic Entropy of Context Length Scaling in LLMs, by Jingzhe Shi and 5 other authors View PDF HTML (experimental) Abstract:Long Context Language Models have drawn great attention in the past few years. There has been work discussing the impact of long context on Language Model performance: some find that long irrelevant context could harm performance, while some experimentally summarize loss reduction by relevant long context as Scaling Laws. This calls for a more thorough understanding of how long context impacts Language Modeling. In this work, we (1) propose to use `Intrinsic Entropy' for explaining the impact of context length on language modeling; and (2) conduct experiments on natural language and synthetic data, validating our proposed theoretical assumptions and deductions. Our theoretical framework can provide practical insights such as establishing that training dataset size dictates an optimal context length and bounds context length scaling for certain cases. We hope our work may inspire new long context Language Models, as well as future work studying the physics of Language Models. Comments: Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) C...