---
title: "Commonsense Knowledge with Negation: A Resource to"
description: "Large language models still struggle with negation, and commonsense knowledge bases mostly ignore it. 0.64 accuracy. 36 references."
authors: ["Zijie Wang", "MohammadHossein Rezaei", "Farzana Rashid", "Eduardo Blanco"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.578/"
doi: "10.18653/v1/2026.findings-acl.578"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-commonsense-knowledge-negation-resource-enhance/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2026-commonsense-knowledge-negation-resource-enhance/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/) / Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding

# Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding

**Authors:** [Zijie Wang](https://md.research.elamur.ai/free/en/authors/zijie-wang/), MohammadHossein Rezaei, Farzana Rashid and [Eduardo Blanco](https://md.research.elamur.ai/free/en/authors/eduardo-blanco/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.578/) · [PDF](https://aclanthology.org/2026.findings-acl.578.pdf) · [DOI 10.18653/v1/2026.findings-acl.578](https://doi.org/10.18653/v1/2026.findings-acl.578) · [OpenAlex](https://openalex.org/W7166837099)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Large language models still struggle with negation, and commonsense knowledge bases mostly ignore it. The only earlier negated resource, ANION, negates only the "if" side of ATOMIC triples and requires manual annotation of new consequences. This paper extends ATOMIC and ANION automatically. Llama 3.1 70B adds the cue *not* to the if event, to the then event, or to both events at once, which yields ¬ATOMIC and ¬ANION, together holding more than 2M if-then triples.

Some negated triples remain plausible and others become contradictory or unclear. To sort them, the authors fine-tune a dedicated Llama-based judge that labels each triple Valid, Invalid, or Ambiguous. This judge beats few-shot GPT-4o and Claude Sonnet 4 on a new human-annotated benchmark.

The validated triples are then used for further pre-training. This consistently improves Llama 3.1 8B and Qwen2 7B across five negation-focused benchmarks. The work shows that cheap, automatically produced negated commonsense data can strengthen negation handling in general-purpose models.

## Key points
- **Resources:** ¬ATOMIC and ¬ANION contain over 2M negated triples. A manual check of 200 generated negations found 99% syntactically correct. Code and data are on GitHub (wang-zijie/commonsense_with_negation).
- **Validation benchmark:** 7,200 triples, made of 200 per ATOMIC relation plus three negated variants of each. Two annotators labelled them, with inter-annotator agreement of 0.62.
- **Judge performance:** The fine-tuned Llama 3.1 70B judge reaches 0.64 accuracy. Few-shot baselines score lower: Claude Sonnet 4 at 0.56 and GPT-4o at 0.54.
- **Downstream gains:** The models were evaluated on CondaQA, RTE-Neg, SNLI-Neg, MNLI-Neg, and NevIR. After pre-training on 89K ¬ATOMIC + 76K ¬ANION triples, Llama 3.1 8B improves on RTE-Neg from 60.0 to 81.3 and on NevIR from 30.6 to 38.2.
- **Ablations:**
  - About 20K triples nearly saturate the NLI tasks, while NevIR keeps improving with more data.
  - Training on randomly labelled data hurts performance, confirming that the judge matters.
  - The study covers only the cue *not* and only 7B/8B models with 4-bit quantization.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Arizona](https://md.research.elamur.ai/free/en/universities/university-of-arizona/) — University, Tucson, United States · 39 works · 10,603 citations
- [University of North Carolina at Asheville](https://md.research.elamur.ai/free/en/universities/university-of-north-carolina-at-asheville/) — University, Asheville, United States · 4 works · 1,502 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works

## Cite

Cite as: Zijie Wang, MohammadHossein Rezaei, Farzana Rashid and Eduardo Blanco. 2026. Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026commonsense,
  title = {{Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding}},
  author = {Zijie Wang, MohammadHossein Rezaei, Farzana Rashid and Eduardo Blanco},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.578},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.578}
}
```

- [← previous CommonMorph: Participatory Morphological Documentation Platform](https://md.research.elamur.ai/free/en/papers/mahmudi-2026-commonmorph-participatory-morphological-documentation-platform/)
- [next → Commonsense Plausibility Estimation Based on Pretrained Models: A Survey](https://md.research.elamur.ai/free/en/papers/cui-2026-commonsense-plausibility-estimation/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/)

## References

36 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
2. Hiba Arnaout, Simon Razniewski, Gerhard Weikum, and Jeff Z. Pan. 2022. Uncommonsense: Informative negative knowledge about everyday concepts. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM ’22, page 37–46, New York, NY, USA. Association for Comput… [find](https://scholar.google.com/scholar?q=Hiba+Arnaout%2C+Simon+Razniewski%2C+Gerhard+Weikum%2C+and+Jeff+Z%2E+Pan%2E+2022%2E+Uncommonsense%3A+Informative+negative+knowledge+about+everyday+concepts%2E+In+Proceedings+of+the+31st+ACM+International+Conference+on)
3. Ron Artstein and Massimo Poesio. 2008. Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4):555–596. [find](https://scholar.google.com/scholar?q=Ron+Artstein+and+Massimo+Poesio%2E+2008%2E+Inter%2Dcoder+agreement+for+computational+linguistics%2E+Computational+Linguistics%2C+34%284%29%3A555%E2%80%93596%2E)
4. Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019. [COMET: Commonsense transformers for automatic knowledge graph construction](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4762–4779, Florence…
5. Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. [A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/). In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632–642, Lisbon, Portugal. Association for Computati…
6. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33:1877–1901.
7. Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017. [SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/). In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 1–14, Vancouver, Canada.…
8. Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The pascal recognising textual entailment challenge. In Proceedings of the First International Conference on Machine Learning Challenges: Evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment, MLCW’05,… [find](https://scholar.google.com/scholar?q=Ido+Dagan%2C+Oren+Glickman%2C+and+Bernardo+Magnini%2E+2005%2E+The+pascal+recognising+textual+entailment+challenge%2E+In+Proceedings+of+the+First+International+Conference+on+Machine+Learning+Challenges%3A+Evaluati)
9. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. [Qlora: Efficient finetuning of quantized llms](https://md.research.elamur.ai/free/en/papers/dettmers-2023-qlora-efficient-finetuning-quantized-llms/). Advances in neural information processing systems, 36:10088–10115.
10. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
11. Radina Dobreva and Frank Keller. 2021. Investigating negation in pre-trained vision-and-language models. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 350–362, Punta Cana, Dominican Republic. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Radina+Dobreva+and+Frank+Keller%2E+2021%2E+Investigating+negation+in+pre%2Dtrained+vision%2Dand%2Dlanguage+models%2E+In+Proceedings+of+the+Fourth+BlackboxNLP+Workshop+on+Analyzing+and+Interpreting+Neural+Networks)
12. Tianqing Fang, Quyet V. Do, Hongming Zhang, Yangqiu Song, Ginny Y. Wong, and Simon See. 2022. PseudoReasoner: Leveraging pseudo labels for commonsense knowledge base population. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3379–3394, Abu Dhabi, United Arab Emirates… [find](https://scholar.google.com/scholar?q=Tianqing+Fang%2C+Quyet+V%2E+Do%2C+Hongming+Zhang%2C+Yangqiu+Song%2C+Ginny+Y%2E+Wong%2C+and+Simon+See%2E+2022%2E+PseudoReasoner%3A+Leveraging+pseudo+labels+for+commonsense+knowledge+base+population%2E+In+Findings+of+the+Ass)
13. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
14. Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, and Minlie Huang. 2020. A knowledge-enhanced pre-training model for commonsense story generation. Transactions of the Association for Computational Linguistics, 8:93–108. [find](https://scholar.google.com/scholar?q=Jian+Guan%2C+Fei+Huang%2C+Zhihao+Zhao%2C+Xiaoyan+Zhu%2C+and+Minlie+Huang%2E+2020%2E+A+knowledge%2Denhanced+pre%2Dtraining+model+for+commonsense+story+generation%2E+Transactions+of+the+Association+for+Computational+Ling)
15. Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, Tiffany Kao, Elizabeth Wei, and Eduardo Blanco. 2020. [An analysis of natural language inference benchmarks through the lens of negation](https://md.research.elamur.ai/free/en/papers/hossain-2020-analysis-natural-language-inference-benchmarks/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9106…
16. Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, and Aaron Courville. 2021. [Understanding by understanding not: Modeling negation in language models](https://md.research.elamur.ai/free/en/papers/hosseini-2021-understanding-understanding-not-modeling-negation/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics:…
17. Myeongjun Jang, Frank Mtumbuka, and Thomas Lukasiewicz. 2022. Beyond distributional hypothesis: Let language models learn meaning-text correspondence. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2030– 2042, Seattle, United States. Association for Computational Lin… [find](https://scholar.google.com/scholar?q=Myeongjun+Jang%2C+Frank+Mtumbuka%2C+and+Thomas+Lukasiewicz%2E+2022%2E+Beyond+distributional+hypothesis%3A+Let+language+models+learn+meaning%2Dtext+correspondence%2E+In+Findings+of+the+Association+for+Computational+)
18. Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, and Yejin Choi. 2021. “I‘m not mad”: Commonsense implications of negation and contradiction. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 43… [find](https://scholar.google.com/scholar?q=Liwei+Jiang%2C+Antoine+Bosselut%2C+Chandra+Bhagavatula%2C+and+Yejin+Choi%2E+2021%2E+%E2%80%9CI%E2%80%98m+not+mad%E2%80%9D%3A+Commonsense+implications+of+negation+and+contradiction%2E+In+Proceedings+of+the+2021+Conference+of+the+North+Amer)
19. Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, and Niranjan Balasubramanian. 2022. Using commonsense knowledge to answer why-questions. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1204–1219, Abu Dhabi… [find](https://scholar.google.com/scholar?q=Yash+Kumar+Lal%2C+Niket+Tandon%2C+Tanvi+Aggarwal%2C+Horace+Liu%2C+Nathanael+Chambers%2C+Raymond+Mooney%2C+and+Niranjan+Balasubramanian%2E+2022%2E+Using+commonsense+knowledge+to+answer+why%2Dquestions%2E+In+Proceedings+of)
20. Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, et al. 2024. From generation to judgment: Opportunities and challenges of llm-as-a-judge. arXiv preprint arXiv:2411.16594. [find](https://scholar.google.com/scholar?q=Dawei+Li%2C+Bohan+Jiang%2C+Liangjie+Huang%2C+Alimohammad+Beigi%2C+Chengshuai+Zhao%2C+Zhen+Tan%2C+Amrita+Bhattacharjee%2C+Yuxuan+Jiang%2C+Canyu+Chen%2C+Tianhao+Wu%2C+et+al%2E+2024%2E+From+generation+to+judgment%3A+Opportunities)
21. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. [Roberta: A robustly optimized bert pretraining approach](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/). arXiv preprint arXiv:1907.11692.
22. Quinn McNemar. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2):153–157. [find](https://scholar.google.com/scholar?q=Quinn+McNemar%2E+1947%2E+Note+on+the+sampling+error+of+the+difference+between+correlated+proportions+or+percentages%2E+Psychometrika%2C+12%282%29%3A153%E2%80%93157%2E)
23. Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 708–718, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rodrigo+Nogueira%2C+Zhiying+Jiang%2C+Ronak+Pradeep%2C+and+Jimmy+Lin%2E+2020%2E+Document+ranking+with+a+pre%2Dtrained+sequence%2Dto%2Dsequence+model%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP)
24. Abhilasha Ravichander, Matt Gardner, and Ana Marasovic. 2022. CONDAQA: A contrastive reading comprehension dataset for reasoning about negation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8729–8755, Abu Dhabi, United Arab Emirates. Association fo… [find](https://scholar.google.com/scholar?q=Abhilasha+Ravichander%2C+Matt+Gardner%2C+and+Ana+Marasovic%2E+2022%2E+CONDAQA%3A+A+contrastive+reading+comprehension+dataset+for+reasoning+about+negation%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Meth)
25. MohammadHossein Rezaei and Eduardo Blanco. 2024. Paraphrasing in affirmative terms improves negation understanding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 602–615, Bangkok, Thailand. Association for Computational Lin… [find](https://scholar.google.com/scholar?q=MohammadHossein+Rezaei+and+Eduardo+Blanco%2E+2024%2E+Paraphrasing+in+affirmative+terms+improves+negation+understanding%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+Lingu)
26. MohammadHossein Rezaei and Eduardo Blanco. 2025. Making language models robust against negation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 8123–8142, Albuqu… [find](https://scholar.google.com/scholar?q=MohammadHossein+Rezaei+and+Eduardo+Blanco%2E+2025%2E+Making+language+models+robust+against+negation%2E+In+Proceedings+of+the+2025+Conference+of+the+Nations+of+the+Americas+Chapter+of+the+Association+for+Com)
27. Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 2019. Atomic: An atlas of machine commonsense for if-then reasoning. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 3027–30… [find](https://scholar.google.com/scholar?q=Maarten+Sap%2C+Ronan+Le+Bras%2C+Emily+Allaway%2C+Chandra+Bhagavatula%2C+Nicholas+Lourie%2C+Hannah+Rashkin%2C+Brendan+Roof%2C+Noah+A+Smith%2C+and+Yejin+Choi%2E+2019%2E+Atomic%3A+An+atlas+of+machine+commonsense+for+if%2Dthen+r)
28. Rituraj Singh, Rahul Kumar, and Vivek Sridhar. 2023. NLMs: Augmenting negation in language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13104–13116, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rituraj+Singh%2C+Rahul+Kumar%2C+and+Vivek+Sridhar%2E+2023%2E+NLMs%3A+Augmenting+negation+in+language+models%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2023%2C+pages+13104%E2%80%9313116%2C+Singapor)
29. Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. Conceptnet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI conference on artificial intelligence, volume 31. [find](https://scholar.google.com/scholar?q=Robyn+Speer%2C+Joshua+Chin%2C+and+Catherine+Havasi%2E+2017%2E+Conceptnet+5%2E5%3A+An+open+multilingual+graph+of+general+knowledge%2E+In+Proceedings+of+the+AAAI+conference+on+artificial+intelligence%2C+volume+31%2E)
30. Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. [CommonsenseQA: A question answering challenge targeting commonsense knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
31. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. [Llama: Open and efficient foundation language models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/). arXiv preprint arXiv:2302.13971.
32. Orion Weller, Dawn Lawrie, and Benjamin Van Durme. 2024. NevIR: Negation in neural information retrieval. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2274–2287, St. Julian’s, Malta. Association for Comp… [find](https://scholar.google.com/scholar?q=Orion+Weller%2C+Dawn+Lawrie%2C+and+Benjamin+Van+Durme%2E+2024%2E+NevIR%3A+Negation+in+neural+information+retrieval%2E+In+Proceedings+of+the+18th+Conference+of+the+European+Chapter+of+the+Association+for+Computati)
33. Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2022. [Symbolic knowledge distillation: from general language models to commonsense models](https://md.research.elamur.ai/free/en/papers/west-2022-symbolic-knowledge-distillation-general-language/). In Proceedings of the 2022 Conference of the North American Chapter of the Associa…
34. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A broad-coverage challenge corpus for sentence understanding through inference](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Pa…
35. An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Juny… [find](https://scholar.google.com/scholar?q=An+Yang%2C+Baosong+Yang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chang+Zhou%2C+Chengpeng+Li%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Guanting+Dong%2C+Haoran+Wei%2C+Huan+Lin%2C+Jialong+Tang%2C+Jialin+Wang%2C+Jian+Yang%2C+Jian)
36. Wenting Zhao, Justin Chiu, Jena Hwang, Faeze Brahman, Jack Hessel, Sanjiban Choudhury, Yejin Choi, Xiang Li, and Alane Suhr. 2024. UNcommonsense reasoning: Abductive reasoning about uncommon situations. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Comput… [find](https://scholar.google.com/scholar?q=Wenting+Zhao%2C+Justin+Chiu%2C+Jena+Hwang%2C+Faeze+Brahman%2C+Jack+Hessel%2C+Sanjiban+Choudhury%2C+Yejin+Choi%2C+Xiang+Li%2C+and+Alane+Suhr%2E+2024%2E+UNcommonsense+reasoning%3A+Abductive+reasoning+about+uncommon+situation)

*Updated 1 November 2026*