# "I'm Not Mad": Commonsense Implications of Negation and Contradiction

arXiv: https://arxiv.org/abs/2104.06511 · HTML: https://mlsanity.com/paper/2104.06511/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly](https://mlsanity.com/paper/1911.03343/) (2019)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [WinoGrande: An Adversarial Winograd Schema Challenge at Scale](https://mlsanity.com/paper/1907.10641/) (2019)
- [Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs](https://mlsanity.com/paper/2010.11465/) (2020)
- [Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2005.00646/) (2020)
- [Counterfactual Story Reasoning and Generation](https://mlsanity.com/paper/1909.04076/) (2019)
- [Logic-Guided Data Augmentation and Regularization for Consistent Question Answering](https://mlsanity.com/paper/2004.10157/) (2020)
- [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://mlsanity.com/paper/1904.11544/) (2019)
- [Towards an Atlas of Cultural Commonsense for Machine Reasoning](https://mlsanity.com/paper/2009.05664/) (2020)
- [It's not a Non-Issue: Negation as a Source of Error in Machine Translation](https://mlsanity.com/paper/2010.05432/) (2020)

## Built on by (most cited first, 26 of 26)

- [ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://mlsanity.com/paper/2303.16421/) (2023)
- [Synthetic Disinformation Attacks on Automated Fact Verification Systems](https://mlsanity.com/paper/2202.09381/) (2022)
- [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://mlsanity.com/paper/2211.00295/) (2022)
- [PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives](https://mlsanity.com/paper/2305.02364/) (2023)
- [RECKONING: Reasoning through Dynamic Knowledge Encoding](https://mlsanity.com/paper/2305.06349/) (2023)
- [Discovering Knowledge-Critical Subnetworks in Pretrained Language Models](https://mlsanity.com/paper/2310.03084/) (2023)
- [Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems](https://mlsanity.com/paper/2209.03755/) (2022)
- [Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models](https://mlsanity.com/paper/2305.17311/) (2023)
- [ComFact: A Benchmark for Linking Contextual Commonsense Knowledge](https://mlsanity.com/paper/2210.12678/) (2022)
- [MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models](https://mlsanity.com/paper/2407.17379/) (2024)
- [UnCommonSense: Informative Negative Knowledge about Everyday Concepts](https://mlsanity.com/paper/2208.09292/) (2022)
- [Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding](https://mlsanity.com/paper/2210.14486/) (2022)
- [EvEntS ReaLM: Event Reasoning of Entity States via Language Models](https://mlsanity.com/paper/2211.05392/) (2022)
- [A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations](https://mlsanity.com/paper/2205.11467/) (2022)
- [Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding](https://mlsanity.com/paper/2506.14397/) (2025)
- [PInKS: Preconditioned Commonsense Inference with Minimal Supervision](https://mlsanity.com/paper/2206.07920/) (2022)
- [kogito: A Commonsense Knowledge Inference Toolkit](https://mlsanity.com/paper/2211.08451/) (2022)
- ["It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems](https://mlsanity.com/paper/2109.07576/) (2021)
- [CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models](https://mlsanity.com/paper/2312.12853/) (2023)
- [Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts](https://mlsanity.com/paper/2202.00848/) (2022)
- [DiffuCOMET: Contextual Commonsense Knowledge Diffusion](https://mlsanity.com/paper/2402.17011/) (2024)
- [Negation-Instance Based Evaluation of End-to-End Negation Resolution](https://mlsanity.com/paper/2109.10013/) (2021)
- [Preconditioned Visual Language Inference with Weak Supervision](https://mlsanity.com/paper/2306.01753/) (2023)
- [Negated Complementary Commonsense using Large Language Models](https://mlsanity.com/paper/2307.06794/) (2023)
- [MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Contextual Reasoning](https://mlsanity.com/paper/2603.01055/) (2026)
- [Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding](https://mlsanity.com/paper/2604.19921/) (2026)

## Abstract (arXiv)

Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., "I'm mad at you"), humans can reason about the varying shades of contradictory statements ranging from straightforward negations ("I'm not mad at you") to commonsense contradictions ("I'm happy"). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in nontrivial ways. For example, while "I'm mad" implies "I'm unhappy about something," negating the premise (i.e., "I'm not mad") does not necessarily negate the corresponding commonsense implications. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION1, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.