Data Foundations of Long-Context Language Models: A Survey
Paper · 2026 · Zechen Sun, Yuyang Sun, Zhaochen Su et al.
Summary
Most earlier surveys of long-context language models (LCMs, here meaning models with windows above 128K tokens) focus on architecture and efficient training, so the role of data has been largely overlooked. This TACL 2026 survey by Sun et al. takes a data-centric view instead. It first defines what useful long-context data should offer: coherence, many dependencies between distant passages, and variety across domains and formats such as code and tables. It then reviews how such data is obtained, either by collecting naturally long texts or by constructing sequences artificially. Each training-data design is linked to one of five target skills (language modeling, retrieval, reasoning, aggregation, long-form generation) and to benchmarks that test the same skill. Datasets and benchmarks are compared in summary tables. The paper releases no new model or corpus. Its contribution is a taxonomy with practical guidance that helps practitioners choose data recipes and evaluation suites. It also identifies open problems for the field.
Key points
- Natural sources reviewed include books (Books3, BookCorpus2), Wikipedia and scientific literature. Examples are LADM, which draws long documents from book corpora, and LongPAS, which builds Wikipedia knowledge graphs to create multi-hop reasoning chains.
- Synthetic construction falls into three families:
- random concatenation (Code Llama, ProLong);
- semantic aggregation of related documents (ICLM, Quest);
- LLM-based synthesis (WildLong, SEALONG).
- The tables compare training sets and benchmarks on five dimensions: where the data comes from, which training stage it serves, how it was built, how long its sequences are, and which capabilities it targets.
- Benchmarks covered include LongBench and HELMET (general), RULER and NeedleBench (retrieval), and Oolong and MIR-Bench (aggregation). For long-form generation, which the paper treats as outputs beyond 32K tokens, it covers LongWeave and UNCLE.
- Proposed future directions:
- a unified way to score data quality (information density, dependency distance, cross-document coherence);
- scaling laws for mixing short and long data, including whether packed short documents can substitute for genuinely long ones;
- dynamic benchmarks that adjust their own difficulty.
Institutions
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works
🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works
Cite
Cite as Zechen Sun, Yuyang Sun, Zhaochen Su, Zecheng Tang, Juntao Li, Ao Zhou, Wenliang Chen and Min Zhang. 2026. Data Foundations of Long-Context Language Models: A Survey. Transactions of the Association for Computational Linguistics.
@article{sun2026data,
title = {{Data Foundations of Long-Context Language Models: A Survey}},
author = {Zechen Sun, Yuyang Sun, Zhaochen Su, Zecheng Tang, Juntao Li, Ao Zhou, Wenliang Chen and Min Zhang},
journal = {Transactions of the Association for Computational Linguistics},
year = {2026},
doi = {10.1162/tacl.a.775},
url = {https://doi.org/10.1162/tacl.a.775}
}References
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