Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding

• URL: http://arxiv.org/abs/2609.13685v1
• Date: Sat, 12 Sep 2026 03:39:27 GMT
• Title: Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understanding
• Abstract summary: Negation remains a longstanding challenge for both language models (LMs) and large language models (LLMs)<n>We construct NegCue, a large-scale dataset containing over 1.8M samples spanning single-word, multi-word, and affixal negation.
• Score: 9.410488940526529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP [57.33324843049638]
  We introduce data generation pipelines that employ a large language model (LLM) and a multimodal LLM to produce negation-inclusive captions.<n>Fine-tuning CLIP with data generated from our pipelines, we develop NegationCLIP, which enhances negation awareness while preserving the generality.<n>Experiments on various CLIP architectures validate the effectiveness of our data generation pipelines in enhancing CLIP's ability to perceive negation accurately.
  arXiv  Detail & Related papers  (2025-01-19T01:17:05Z)
• Vision-Language Models Do Not Understand Negation [50.27667000027403]
  NegBench is a benchmark designed to evaluate negation understanding across 18 task variations and $79$k examples.<n>We show that this approach can result in a 10% increase in recall on negated queries and a 28% boost in accuracy on multiple-choice questions with negated captions.
  arXiv  Detail & Related papers  (2025-01-16T09:55:42Z)
• Generating Diverse Negations from Affirmative Sentences [0.999726509256195]
  Negations are important in real-world applications as they encode negative polarity in verb phrases, clauses, or other expressions. We propose NegVerse, a method that tackles the lack of negation datasets by producing a diverse range of negation types. We provide new rules for masking parts of sentences where negations are most likely to occur, based on syntactic structure. We also propose a filtering mechanism to identify negation cues and remove degenerate examples, producing a diverse range of meaningful perturbations.
  arXiv  Detail & Related papers  (2024-10-30T21:25:02Z)
• Revisiting subword tokenization: A case study on affixal negation in large language models [57.75279238091522]
  We measure the impact of affixal negation on modern English large language models (LLMs) We conduct experiments using LLMs with different subword tokenization methods. We show that models can, on the whole, reliably recognize the meaning of affixal negation.
  arXiv  Detail & Related papers  (2024-04-03T03:14:27Z)
• This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models [4.017326849033009]
  We try to clarify the reasons for the sub-optimal performance of large language models understanding negation. We introduce a large semi-automatically generated dataset of circa 400,000 descriptive sentences about commonsense knowledge. We have used our dataset with the largest available open LLMs in a zero-shot approach to grasp their generalization and inference capability.
  arXiv  Detail & Related papers  (2023-10-24T15:38:21Z)
• Language models are not naysayers: An analysis of language models on negation benchmarks [58.32362243122714]
  We evaluate the ability of current-generation auto-regressive language models to handle negation. We show that LLMs have several limitations including insensitivity to the presence of negation, an inability to capture the lexical semantics of negation, and a failure to reason under negation.
  arXiv  Detail & Related papers  (2023-06-14T01:16:37Z)
• Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation [59.307534363825816]
  Negation is poorly captured by current language models, although the extent of this problem is not widely understood. We introduce a natural language inference (NLI) test suite to enable probing the capabilities of NLP methods.
  arXiv  Detail & Related papers  (2022-10-06T23:39:01Z)
• Improving negation detection with negation-focused pre-training [58.32362243122714]
  Negation is a common linguistic feature that is crucial in many language understanding tasks. Recent work has shown that state-of-the-art NLP models underperform on samples containing negation. We propose a new negation-focused pre-training strategy, involving targeted data augmentation and negation masking.
  arXiv  Detail & Related papers  (2022-05-09T02:41:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.