aiqbench analysis
This study addresses the persistent difficulty LLMs face in processing negation by introducing NegCue, a comprehensive dataset covering single-word, multi-word, and affixal forms. The research reveals that pre-training on affixal negations drives the most significant performance gains across benchmarks, whereas traditional single-word cues offer more modest improvements. These findings provide actionable insights for refining language model training strategies to better handle linguistic nuances essential for robust AI reasoning.
Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding
arXiv:2609.13685v1 Announce Type: cross 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 remain modest. Moreover, our results demonstrate that further pre-training improves negation understanding for both LMs and LLMs.
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 remain modest. Moreover, our results demonstrate that further pre-training improves negation understanding for both LMs and LLMs.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.13685 [cs.CL]
(or arXiv:2609.13685v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.13685 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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