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        <title>LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking</title>
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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Vera</namePart>
            <namePart type="family">Demberg</namePart>
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        <name type="personal">
            <namePart type="given">Lluís</namePart>
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    <abstract>We present LingGen, a controlled text generation model that allows fine-grained control over a large number of real-valued linguistic attributes. It encodes target attribute values with a dedicated linguistic attribute encoder and conditions the language model by injecting the resulting representation into the language model using the beginning-of-sequence (BOS) embeddings. To improve robustness when controlling different attribute subsets, we introduce P-MASKING, which samples per-example attribute masking rates from a truncated Pareto distribution during training. Across 1-40 control attributes, LingGen achieves the lowest average control error among evaluated methods, while remaining efficient at inference and receiving the highest fluency scores in human evaluation. Ablations show that Pareto-sampled masking and BOS-based injection are effective choices compared to alternative masking and integration variants.</abstract>
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        <date>2026-03</date>
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            <start>1925</start>
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%0 Conference Proceedings
%T LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking
%A Elgaar, Mohamed
%A Amiri, Hadi
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F elgaar-amiri-2026-linggen
%X We present LingGen, a controlled text generation model that allows fine-grained control over a large number of real-valued linguistic attributes. It encodes target attribute values with a dedicated linguistic attribute encoder and conditions the language model by injecting the resulting representation into the language model using the beginning-of-sequence (BOS) embeddings. To improve robustness when controlling different attribute subsets, we introduce P-MASKING, which samples per-example attribute masking rates from a truncated Pareto distribution during training. Across 1-40 control attributes, LingGen achieves the lowest average control error among evaluated methods, while remaining efficient at inference and receiving the highest fluency scores in human evaluation. Ablations show that Pareto-sampled masking and BOS-based injection are effective choices compared to alternative masking and integration variants.
%U https://aclanthology.org/2026.eacl-long.85/
%P 1925-1942

Markdown (Informal)

[LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking](https://aclanthology.org/2026.eacl-long.85/) (Elgaar & Amiri, EACL 2026)

• LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking (Elgaar & Amiri, EACL 2026)

ACL

• Mohamed Elgaar and Hadi Amiri. 2026. LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1925–1942, Rabat, Morocco. Association for Computational Linguistics.