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A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
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Adina Williams, Nikita Nangia, Samuel Bowman. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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“NLI measures have been used to measure faithfulness in summarization, where Maynez et al. (2020) use a textual entailment classifier trained on MNLI (Williams et al., 2018) to determine if a summary contradicts a context or not.”
From SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023) · cites this paper as Williams et al., 2018
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“InferSent (Conneau et al., 2017) uses labeled data of the Stanford Natural Language Inference dataset (Bowman et al., 2015) and the MultiGenre NLI dataset (Williams et al., 2018) to train a siamese BiLSTM network with max-pooling over the output.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Williams et al., 2018
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“Most prior entailment datasets have been human-authored (Bowman et al., 2015;Williams et al., 2018), which is known to introduce artifacts (Gururangan et al., 2018), or collected in the wild (Kamoi et al., 2023), which is challenging to scale.”
From MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (2024) · cites this paper as Williams et al., 2018
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“This released mixture uses SNLI, MNLI, FEVER, SciTail, PAWS, and VitaminC (Bowman et al., 2015;Williams et al., 2018;Thorne et al., 2018;Khot et al., 2018;Zhang et al., 2019;Schuster et al., 2021), rather than the original TRUE paper's ANLI recipe.”
From The Attribution-Compression Frontier in Retrieval-Augmented Generation (2026) · cites this paper as Williams et al., 2018
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Hu, Z., Wang, Z., Wang, Y., & Tan, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119492 · Cited by 30
A Multi-Level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
Li, S., Hu, X., Li, L., Liu, A., Wen, L., & Yu, P. S. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3270771 · Cited by 30
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression
Wu, S., Chen, H., Quan, X., Wang, Q., & Wang, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.471 · Cited by 30
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Levy, S., Allaway, E., Subbiah, M., Chilton, L. B., Patton, D. U., McKeown, K. R., & Wang, W. Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.154 · Cited by 30
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
Du, M., Mukherjee, S., Cheng, Y., Shokouhi, M., Xia, H., & Awadallah, A. H. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.129 · Cited by 30
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement LearningPreprint
Chan, H., Wu, Y., Kiros, J., Fidler, S., & Ba, J. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04546 · Cited by 30
Optimal Subarchitecture Extraction For BERTPreprint
de Wynter, A., & Perry, D. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10499 · Cited by 30
Continuum: Simple Management of Complex Continual Learning ScenariosPreprint
Douillard, A., & Lesort, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06253 · Cited by 30
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language ProcessingPreprint
Gui, T., Wang, X., Zhang, Q., Liu, Q., Zou, Y., Zhou, X., Zheng, R., Zhang, C., Wu, Q., Ye, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11441 · Cited by 31
Proceedings of the Third Workshop on Narrative Understanding
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WOODS: Benchmarks for Out-of-Distribution Generalization in Time SeriesPreprint
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NeoBERT: A Next-Generation BERTPreprint
Breton, L. L., Fournier, Q., Mezouar, M. E., Morris, J. X., & Chandar, S. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2502.19587 · Cited by 31
PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine
Zhang, C., Liu, L., Wang, C., Sun, X., Wang, H., Wang, J., & Cai, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29924 · Cited by 30
A comprehensive survey on answer generation methods using NLP
Upadhyay, P., Agarwal, R., Dhiman, S., Sarkar, A., & Chaturvedi, S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100088 · Cited by 29
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Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models
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Infusing Finetuning with Semantic Dependencies
Wu, Z., Peng, H., & Smith, N. A. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00363 · Cited by 29
Towards Zero-Shot Knowledge Distillation for Natural Language Processing
Rashid, A., Lioutas, V., Ghaddar, A., & Rezagholizadeh, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.526 · Cited by 29
Deep Learning Mental Health Dialogue System
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Logiqa 2.0—An improved dataset for logical reasoning in natural language understanding
Liu, H., Liu, J., Cui, L., Teng, Z., Duan, N., Zhou, M., & Zhang, Y. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3293046 · Cited by 26
Making Retrieval-Augmented Language Models Robust to Irrelevant Context
Yoran, O., Wolfson, T., Ram, O., & Berant, J. (2023) · arXiv · doi:10.48550/arxiv.2310.01558 · Cited by 26
WiCE: Real-world entailment for claims in Wikipedia
Kamoi, R., Goyal, T., Rodriguez, J., & Durrett, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.470 · Cited by 26
Spoon, K., Tsai, H., Chen, A., Rasch, M. J., Ambrogio, S., Mackin, C., Fasoli, A., Friz, A. M., Narayanan, P., Stanisavljevic, M., et al. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.675741 · Cited by 26
Metadata
- Authors
- Williams, A., Nangia, N., & Bowman, S.
- Affiliations
- New York University (US)
- ORCID
- Williams, Adina
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 1112-1122
- OpenAlex
- W2607892599
- Cited by
- 3,342
- References
- 34
- Citations by year
- 2026: 94 · 2025: 205 · 2024: 248 · 2023: 651 · 2022: 542 · 2021: 710 · 2020: 471 · 2019: 282 · 2018: 118
- FWCI
- 161.45
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Tencent
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- sentence comprehension, inference, natural language understanding, commonsense reasoning, question answering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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