Revisiting the Systematicity in Negation in the Era of In-Context Learning

• URL: http://arxiv.org/abs/2606.16867v1
• Date: Mon, 15 Jun 2026 15:44:15 GMT
• Title: Revisiting the Systematicity in Negation in the Era of In-Context Learning
• Abstract summary: We analyze systematicity regarding understanding of negation from two perspectives: behavioral systematicity and representational systematicity.<n>For behavioral systematicity, we confirm that through demonstrations and in-context learning, LLMs can recognize negation expressions and scope within sentences to some extent, but they fail to achieve perfect performance.<n>For representational systematicity, we analyze the extent to which function vectors can be robustly constructed from in-context examples for tasks that are essential to understanding negation.
• Score: 16.384578665588936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the meaning of negated sentences remains one of the challenges for language models, even in the era of large language models (LLMs). We analyze systematicity regarding LLM understanding of negation from two perspectives: behavioral systematicity and representational systematicity. For behavioral systematicity, we confirm that through demonstrations and in-context learning, LLMs can recognize negation expressions and scope within sentences to some extent, but they fail to achieve perfect performance. In particular, the difficulty of the negation scope recognition for models varies depending on the output format. For representational systematicity, we analyze the extent to which function vectors can be robustly constructed from in-context examples for tasks that are essential to understanding negation. The experiments suggest that while function vectors can be composed for negation cue extraction tasks, extracting function vectors for recognizing scope is more challenging.

Related papers

• Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution [44.39196719594625]
  We show that negation does not form a linearly or non-linearly separable class in the latent spaces of standard vision-language models.<n>We propose a novel cross-modal attention architecture that explicitly models inter-modal dependencies.
  arXiv  Detail & Related papers  (2026-07-20T09:08:51Z)
• Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs [21.808192032202232]
  We introduce RS-Neg, the first benchmark to evaluate negation understanding across region-level to scene-level tasks.<n>We propose NeFo, a novel test-time learning method that explicitly incorporates the logical role of negation into the model optimization.
  arXiv  Detail & Related papers  (2026-06-18T12:46:56Z)
• How Language Models Process Negation [56.41361273288279]
  We study how Large Language Models (LLMs) process negation mechanistically.<n>Our work deepens the understanding of LLMs' internals, highlighting construction-dominant computations and the coexistence of competing mechanisms.
  arXiv  Detail & Related papers  (2026-05-04T18:17:05Z)
• Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning [87.15765427638195]
  We introduce D-Negation, a new dataset that provides objects annotated with both positive and negative semantic descriptions.<n>We propose a grouped opposition-based learning framework that learns negation-aware representations from limited samples.<n>By fine-tuning fewer than 10 percent of the model parameters, our approach achieves improvements of up to 4.4 mAP and 5.7 mAP on positive and negative semantic evaluations.
  arXiv  Detail & Related papers  (2026-03-13T03:21:48Z)
• Farther the Shift, Sparser the Representation: Analyzing OOD Mechanisms in LLMs [100.02824137397464]
  We investigate how Large Language Models adapt their internal representations when encountering inputs of increasing difficulty.<n>We reveal a consistent and quantifiable phenomenon: as task difficulty increases, the last hidden states of LLMs become substantially sparser.<n>This sparsity--difficulty relation is observable across diverse models and domains.
  arXiv  Detail & Related papers  (2026-03-03T18:48:15Z)
• 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)
• Strong hallucinations from negation and how to fix them [2.1178416840822027]
  We show that our approach improves model performance in cloze prompting and natural language inference tasks with negation without requiring training on sparse negative data. We call such responses textitstrong hallucinations and prove that they follow from an LM's computation of its internal representations for logical operators and outputs from those representations.
  arXiv  Detail & Related papers  (2024-02-16T10:11:20Z)
• Uncertainty Quantification for In-Context Learning of Large Language Models [52.891205009620364]
  In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion.
  arXiv  Detail & Related papers  (2024-02-15T18:46:24Z)
• 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)

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.