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        <title>Sedimented Use and Borrowed Competence: A Relational Framework for Language Models</title>
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            <namePart type="family">Sousa-Silva</namePart>
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        <name type="personal">
            <namePart type="given">Henrique</namePart>
            <namePart type="family">Lopes Cardoso</namePart>
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            <namePart type="given">Maarit</namePart>
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    <abstract>Language models learn from records of language that has already been used, and those records preserve more than lexical and syntactic form: they carry traces of communicative goals, genres, institutions, social norms, testimony, correction and practical activity. We develop a relational account of language-model competence in which behaviour is understood through the sources of guidance that shape interpretation and action, and we call text-mediated access to inherited semantic-pragmatic structure borrowed competence. Borrowed competence sits within a wider space of relations to language that includes formal signal, sedimented use, evidence-sensitive verification, addressive uptake, situated coping and accountable participation, each of which can be characterized by a source of guidance, a channel through which that guidance becomes available, a timescale over which it persists and an intervention that tests whether it mattered. This view yields an evaluation method based on discriminant interventions, explicit system boundaries and relational profiles rather than final-answer accuracy alone. A fully specified paired-archive example shows how a broad claim about pragmatic competence becomes an inspectable test, while the same logic extends to retrieval, memory, multimodal and tool-using systems and institutionally embedded agents. The resulting framework treats language competence as relational and compositional, providing a common vocabulary for studying how archives, evidence, interlocutors, environments, memory, tools and institutions jointly shape language-guided behaviour.</abstract>
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%0 Conference Proceedings
%T Sedimented Use and Borrowed Competence: A Relational Framework for Language Models
%A Long, Yanan
%Y Sousa-Silva, Rui
%Y Lopes Cardoso, Henrique
%Y Koponen, Maarit
%Y Pareja-Lora, Antonio
%S Proceedings of the 3rd Workshop on Language Understanding in the Human-Machine Era (LUHME 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-471-2
%F long-2026-sedimented
%X Language models learn from records of language that has already been used, and those records preserve more than lexical and syntactic form: they carry traces of communicative goals, genres, institutions, social norms, testimony, correction and practical activity. We develop a relational account of language-model competence in which behaviour is understood through the sources of guidance that shape interpretation and action, and we call text-mediated access to inherited semantic-pragmatic structure borrowed competence. Borrowed competence sits within a wider space of relations to language that includes formal signal, sedimented use, evidence-sensitive verification, addressive uptake, situated coping and accountable participation, each of which can be characterized by a source of guidance, a channel through which that guidance becomes available, a timescale over which it persists and an intervention that tests whether it mattered. This view yields an evaluation method based on discriminant interventions, explicit system boundaries and relational profiles rather than final-answer accuracy alone. A fully specified paired-archive example shows how a broad claim about pragmatic competence becomes an inspectable test, while the same logic extends to retrieval, memory, multimodal and tool-using systems and institutionally embedded agents. The resulting framework treats language competence as relational and compositional, providing a common vocabulary for studying how archives, evidence, interlocutors, environments, memory, tools and institutions jointly shape language-guided behaviour.
%U https://aclanthology.org/2026.luhme-1.26/
%P 389-399

Markdown (Informal)

[Sedimented Use and Borrowed Competence: A Relational Framework for Language Models](https://aclanthology.org/2026.luhme-1.26/) (Long, LUHME 2026)

• Sedimented Use and Borrowed Competence: A Relational Framework for Language Models (Long, LUHME 2026)

ACL

• Yanan Long. 2026. Sedimented Use and Borrowed Competence: A Relational Framework for Language Models. In Proceedings of the 3rd Workshop on Language Understanding in the Human-Machine Era (LUHME 2026), pages 389–399, Budapest, Hungary. Association for Computational Linguistics.