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        <title>Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding</title>
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    <abstract>Negation remains a longstanding challenge for both language models (LMs) and large language models (LLMs). Prior work mainly focuses on a small set of high-frequency single-word negation cues, such as “not” and “never”, with limited exploration of broader negation types and modern LLMs. To address this gap, we construct NegCue, a large-scale dataset containing over 1.8M samples spanning single-word, multi-word, and affixal negation with more than 200 unique cues. We further pre-train both encoder-only LMs and LLMs on NegCue to investigate how different negation types affect negation understanding. Experiments on five downstream benchmarks show that negation types contribute unevenly to performance gains under the same training scale. In particular, affixal negation yields the largest improvements, while the gains from the commonly studied single-word negation are remain modest. Moreover, our results demonstrate that further pre-training improves negation understanding for both LMs and LLMs.</abstract>
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%0 Conference Proceedings
%T Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding
%A Tan, Tian
%A Blanco, Eduardo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tan-blanco-2026-negation
%X Negation remains a longstanding challenge for both language models (LMs) and large language models (LLMs). Prior work mainly focuses on a small set of high-frequency single-word negation cues, such as “not” and “never”, with limited exploration of broader negation types and modern LLMs. To address this gap, we construct NegCue, a large-scale dataset containing over 1.8M samples spanning single-word, multi-word, and affixal negation with more than 200 unique cues. We further pre-train both encoder-only LMs and LLMs on NegCue to investigate how different negation types affect negation understanding. Experiments on five downstream benchmarks show that negation types contribute unevenly to performance gains under the same training scale. In particular, affixal negation yields the largest improvements, while the gains from the commonly studied single-word negation are remain modest. Moreover, our results demonstrate that further pre-training improves negation understanding for both LMs and LLMs.
%U https://aclanthology.org/2026.emnlp-main.1673/
%P 38056-38076

Markdown (Informal)

[Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding](https://aclanthology.org/2026.emnlp-main.1673/) (Tan & Blanco, EMNLP 2026)

• Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding (Tan & Blanco, EMNLP 2026)

ACL

• Tian Tan and Eduardo Blanco. 2026. Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38056–38076, Budapest, Hungary. Association for Computational Linguistics.