<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sun-etal-2026-data">
    <titleInfo>
        <title>Data Foundations of Long-Context Language Models: A Survey</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zechen</namePart>
        <namePart type="family">Sun</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yuyang</namePart>
        <namePart type="family">Sun</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhaochen</namePart>
        <namePart type="family">Su</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zecheng</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Juntao</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ao</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wenliang</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Min</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>As the context window of Large Language Models (LLMs) continues to expand, the data required to effectively train and evaluate these capabilities remains underexplored. With existing research primarily focuses on architectural optimization, there is a need for a systematic, data-centric review. This survey bridges this gap by investigating the data foundations of Long-Context Language Models (LCMs). We begin by examining current data strategies alongside their strengths and limitations, mapping the required data to desired model capabilities. Building on this, we explore how targeted training data designs drive core, often interconnected skills such as retrieval, reasoning, and aggregation. Furthermore, we analyze the evaluation landscape, illustrating how selecting appropriate benchmarks is crucial for probing capability boundaries and guiding effective model selection. Finally, we synthesize actionable guidelines for data construction and outline critical future directions to propel the advancement of long-context language models, including quantifying data quality, establishing scaling laws for length distributions, and developing dynamic evaluation frameworks.</abstract>
    <identifier type="citekey">sun-etal-2026-data</identifier>
    <identifier type="doi">10.1162/tacl.a.775</identifier>
    <location>
        <url>https://aclanthology.org/2026.tacl-1.81/</url>
    </location>
    <part>
        <date>2026</date>
        <detail type="volume"><number>14</number></detail>
        <extent unit="page">
            <start>1803</start>
            <end>1825</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Data Foundations of Long-Context Language Models: A Survey
%A Sun, Zechen
%A Sun, Yuyang
%A Su, Zhaochen
%A Tang, Zecheng
%A Li, Juntao
%A Zhou, Ao
%A Chen, Wenliang
%A Zhang, Min
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F sun-etal-2026-data
%X As the context window of Large Language Models (LLMs) continues to expand, the data required to effectively train and evaluate these capabilities remains underexplored. With existing research primarily focuses on architectural optimization, there is a need for a systematic, data-centric review. This survey bridges this gap by investigating the data foundations of Long-Context Language Models (LCMs). We begin by examining current data strategies alongside their strengths and limitations, mapping the required data to desired model capabilities. Building on this, we explore how targeted training data designs drive core, often interconnected skills such as retrieval, reasoning, and aggregation. Furthermore, we analyze the evaluation landscape, illustrating how selecting appropriate benchmarks is crucial for probing capability boundaries and guiding effective model selection. Finally, we synthesize actionable guidelines for data construction and outline critical future directions to propel the advancement of long-context language models, including quantifying data quality, establishing scaling laws for length distributions, and developing dynamic evaluation frameworks.
%R 10.1162/tacl.a.775
%U https://aclanthology.org/2026.tacl-1.81/
%U https://doi.org/10.1162/tacl.a.775
%P 1803-1825

Markdown (Informal)

[Data Foundations of Long-Context Language Models: A Survey](https://aclanthology.org/2026.tacl-1.81/) (Sun et al., TACL 2026)

• Data Foundations of Long-Context Language Models: A Survey (Sun et al., TACL 2026)

ACL

• 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, 14:1803–1825.