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        <title>KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates</title>
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        <namePart type="given">Yudong</namePart>
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            <title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Viviane</namePart>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>Standard Large Language Model (LLM) pre-training typically treats corpora as flattened token sequences, often overlooking the real-world context that humans naturally rely on to contextualize information. To bridge this gap, we introduce Knowledge Coordinate Conditioning (KoCo), a simple method that maps every document into a three-dimensional semantic coordinate. By prepending these coordinates as textual prefixes for pre-training, we aim to equip the model with explicit contextual awareness to learn the documents within the real-world knowledge structure. Experiment results demonstrate that KoCo significantly enhances performance across 10 downstream tasks and accelerates pre-training convergence by approximately 30%. Furthermore, our analysis indicates that explicitly modeling knowledge coordinates helps the model distinguish stable facts from noise, effectively mitigating hallucination in generated outputs.</abstract>
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%0 Conference Proceedings
%T KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates
%A Li, Yudong
%A Cai, Jiawei
%A Shen, Linlin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-koco
%X Standard Large Language Model (LLM) pre-training typically treats corpora as flattened token sequences, often overlooking the real-world context that humans naturally rely on to contextualize information. To bridge this gap, we introduce Knowledge Coordinate Conditioning (KoCo), a simple method that maps every document into a three-dimensional semantic coordinate. By prepending these coordinates as textual prefixes for pre-training, we aim to equip the model with explicit contextual awareness to learn the documents within the real-world knowledge structure. Experiment results demonstrate that KoCo significantly enhances performance across 10 downstream tasks and accelerates pre-training convergence by approximately 30%. Furthermore, our analysis indicates that explicitly modeling knowledge coordinates helps the model distinguish stable facts from noise, effectively mitigating hallucination in generated outputs.
%U https://aclanthology.org/2026.acl-long.1111/
%P 24222-24235

Markdown (Informal)

[KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates](https://aclanthology.org/2026.acl-long.1111/) (Li et al., ACL 2026)

• KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates (Li et al., ACL 2026)

ACL

• Yudong Li, Jiawei Cai, and Linlin Shen. 2026. KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24222–24235, San Diego, California, United States. Association for Computational Linguistics.