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        <title>A Universal Avoidance Method for Diverse Multi-branch Generation</title>
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        <namePart type="given">Kyeongman</namePart>
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    <name type="personal">
        <namePart type="given">Minha</namePart>
        <namePart type="family">Jhang</namePart>
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    <name type="personal">
        <namePart type="given">Kyomin</namePart>
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            <namePart type="given">Viviane</namePart>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>Modern generative models still lack human-level creativity, particularly in multi-branch diversity. Prior approaches to address this problem often incur heavy computation or strong dependency on model architecture. Therefore, we introduce **UAG**(**U**niversal **A**voidance **G**eneration), a model-agnostic and computationally efficient generation strategy that penalizes similarity among previously generated outputs. Thus, UAG can enhance multi-branch diversity across both diffusion and transformer models, with minimal additional computation. In experiments, our method achieves up to 1.9 times higher diversity, runs 4.4 times faster, and requires only 1/64 of the FLOPs compared to state-of-the-art methods.</abstract>
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            <start>15857</start>
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%0 Conference Proceedings
%T A Universal Avoidance Method for Diverse Multi-branch Generation
%A Park, Kyeongman
%A Jhang, Minha
%A Jung, Kyomin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F park-etal-2026-universal
%X Modern generative models still lack human-level creativity, particularly in multi-branch diversity. Prior approaches to address this problem often incur heavy computation or strong dependency on model architecture. Therefore, we introduce **UAG**(**U**niversal **A**voidance **G**eneration), a model-agnostic and computationally efficient generation strategy that penalizes similarity among previously generated outputs. Thus, UAG can enhance multi-branch diversity across both diffusion and transformer models, with minimal additional computation. In experiments, our method achieves up to 1.9 times higher diversity, runs 4.4 times faster, and requires only 1/64 of the FLOPs compared to state-of-the-art methods.
%U https://aclanthology.org/2026.findings-acl.777/
%P 15857-15870

Markdown (Informal)

[A Universal Avoidance Method for Diverse Multi-branch Generation](https://aclanthology.org/2026.findings-acl.777/) (Park et al., Findings 2026)

• A Universal Avoidance Method for Diverse Multi-branch Generation (Park et al., Findings 2026)

ACL

• Kyeongman Park, Minha Jhang, and Kyomin Jung. 2026. A Universal Avoidance Method for Diverse Multi-branch Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 15857–15870, San Diego, California, United States. Association for Computational Linguistics.