mlsanityData to 14 Oct 2026

Paraphrasing in Affirmative Terms Improves Negation Understanding

arXiv 2406.07492 ↗First posted 11 Jun 2024cs.CLcited by 8

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Abstract (arXiv)

Negation is a common linguistic phenomenon. Yet language models face challenges with negation in many natural language understanding tasks such as question answering and natural language inference. In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i.e., paraphrases without negation) to make models more robust against negation. Crucially, our affirmative interpretations are obtained automatically. We show improvements with CondaQA, a large corpus requiring reasoning with negation, and five natural language understanding tasks.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 36 of its 59 references that are in the map. First posted on arXiv 11 Jun 2024.

Builds on

36 of 59 references.

References · 36

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24 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.

  1. 2025Negation: A Pink Elephant in the Large Language Models' Room?cited by 7
  2. 2025Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understandingcited by 5
  3. 2025Making Language Models Robust Against Negationcited by 3
  4. 2024Generating Completions for Broca's Aphasic Sentences Using Large Language Modelscited by 0
  5. 2026Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinementcited by 0
  6. 2026Commonsense Knowledge with Negation: A Resource to Enhance Negation Understandingcited by 0
  7. 2026Not all Negation Cues are Equal: Affixal Negations Yield Better Negation Understandingcited by 0
  8. 2026When Verifiable Counts Depend on Wording: Auditing Wording Robustness in Instruction Followingcited by 0