# Negation: A Pink Elephant in the Large Language Models' Room?

arXiv: https://arxiv.org/abs/2503.22395 · HTML: https://mlsanity.com/paper/2503.22395/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [Faith and Fate: Limits of Transformers on Compositionality](https://mlsanity.com/paper/2305.18654/) (2023)
- [What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models](https://mlsanity.com/paper/1907.13528/) (2019)
- [Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly](https://mlsanity.com/paper/1911.03343/) (2019)
- [Language models are not naysayers: An analysis of language models on negation benchmarks](https://mlsanity.com/paper/2306.08189/) (2023)
- [Vision-Language Models Do Not Understand Negation](https://mlsanity.com/paper/2501.09425/) (2025)
- [Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations](https://mlsanity.com/paper/2403.20312/) (2024)
- [Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation](https://mlsanity.com/paper/2210.03256/) (2022)
- [This is not correct! Negation-aware Evaluation of Language Generation Systems](https://mlsanity.com/paper/2307.13989/) (2023)
- [Suppressing Pink Elephants with Direct Principle Feedback](https://mlsanity.com/paper/2402.07896/) (2024)
- [Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation](https://mlsanity.com/paper/2406.05494/) (2024)
- [LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context Memory of Transformers](https://mlsanity.com/paper/2502.15007/) (2025)
- [Negation, Coordination, and Quantifiers in Contextualized Language Models](https://mlsanity.com/paper/2209.07836/) (2022)
- [Paraphrasing in Affirmative Terms Improves Negation Understanding](https://mlsanity.com/paper/2406.07492/) (2024)
- ["Not not bad" is not "bad": A distributional account of negation](https://mlsanity.com/paper/1306.2158/) (2013)
- [Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation](https://mlsanity.com/paper/2409.00105/) (2024)

## Built on by (most cited first, 7 of 7)

- [Feedback That Backfires: Why Small Language Model Agents Repeat the Call They Just Watched Fail](https://mlsanity.com/paper/2608.23651/) (2026)
- [Flying Pigs, FaR and Beyond: Evaluating LLM Reasoning in Counterfactual Worlds](https://mlsanity.com/paper/2505.22318/) (2025)
- [How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images](https://mlsanity.com/paper/2508.21565/) (2025)
- [Don't Think of the White Bear: Ironic Negation in Transformer Models Under Cognitive Load](https://mlsanity.com/paper/2511.12381/) (2025)
- [Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding](https://mlsanity.com/paper/2601.04693/) (2026)
- [Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries](https://mlsanity.com/paper/2602.12301/) (2026)
- [Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context Reinforcement Learning](https://mlsanity.com/paper/2610.11152/) (2026)

## Abstract (arXiv)

Negations are key to determining sentence meaning, making them essential for logical reasoning. Despite their importance, negations pose a substantial challenge for large language models (LLMs) and remain underexplored. We constructed and published two new textual entailment datasets NoFEVER-ML and NoSNLI-ML in four languages (English, Czech, German, and Ukrainian) with examples differing in negation. It allows investigation of the root causes of the negation problem and its exemplification: how popular LLM model properties and language impact their inability to handle negation correctly. Contrary to previous work, we show that increasing the model size may improve the models' ability to handle negations. Furthermore, we find that both the models' reasoning accuracy and robustness to negation are language-dependent and that the length and explicitness of the premise have an impact on robustness. There is better accuracy in projective language with fixed order, such as English, than in non-projective ones, such as German or Czech. Our entailment datasets pave the way to further research for explanation and exemplification of the negation problem, minimization of LLM hallucinations, and improvement of LLM reasoning in multilingual settings.