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A large annotated corpus for learning natural language inference
Open accessAbstract
Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations.However, machine learning research in this area has been dramatically limited by the lack of large-scale resources.To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning.At 570K pairs, it is two orders of magnitude larger than all other resources of its type.This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.
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“This level of expert disagreement is in line with what is observed in other well-known machine learning benchmarks59–62.”
From Humanity's Last Exam (2025) · cites this paper as [62]
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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 Bowman et al., 2015
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“For natural language inference (NLI) tasks, on most datasets, such as on RTE [116] and SNLI [16], fine-tuned models perform better than LLMs, while on some data such as CB [115], LLMs have obtained comparable performance with fine-tuned models [25].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [16]
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Honovich, O., Choshen, L., Aharoni, R., Neeman, E., Szpektor, I., & Abend, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.619 · Cited by 113
“Only”) and a model trained on the SNLI dataset but evaluated on DNLI (“InferSent SNLI”).”
BackgroundMethodDecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.11698 · Cited by 110
An AI-Based System for Formative and Summative Assessment in Data Science Courses
Vittorini, P., Menini, S., & Tonelli, S. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00230-2 · Cited by 105
UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
Yoneda, T., Mitchell, J., Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the First Workshop on Fact Extraction and VERification (FEVER) · doi:10.18653/v1/w18-5515 · Cited by 101
Evaluating the logical reasoning ability of chatgpt and GPT-4
Liu, H., Ning, R., Teng, Z., Liu, J., Zhou, Q., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2304.03439 · Cited by 100
Reducing Quantity Hallucinations in Abstractive Summarization
Zhao, Z., Cohen, S. B., & Webber, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.203 · Cited by 99
Pellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231214460 · Cited by 97
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
Vulić, I., Gerz, D., Kiela, D., Hill, F., & Korhonen, A. (2016) · arXiv · doi:10.48550/arxiv.1608.02117 · Cited by 90
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Ghaeini, R., Fern, X., & Tadepalli, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1537 · Cited by 91
Data Contamination: From Memorization to Exploitation
Magar, I., & Schwartz, R. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2022.acl-short.18 · Cited by 88
Towards Transparent and Explainable Attention Models
Mohankumar, A. K., Nema, P., Narasimhan, S., Khapra, M. M., Srinivasan, B. V., & Ravindran, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.387 · Cited by 91
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · arXiv · doi:10.48550/arxiv.1908.10084 · Cited by 343
Contrastive Explanations for Model Interpretability
Jacovi, A., Swayamdipta, S., Ravfogel, S., Elazar, Y., Choi, Y., & Goldberg, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.120 · Cited by 83
AlignScore: Evaluating factual consistency with a unified alignment function
Zha, Y., Yang, Y., Li, R., & Hu, Z. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.634 · Cited by 81
Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
Warstadt, A., Mueller, A., Choshen, L., Wilcox, E., Zhuang, C., Ciro, J., Mosquera, R., Paranjabe, B., Williams, A., Linzen, T., et al. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.1 · Cited by 74
“This task bears similarity to natural language inference (Dagan et al., 2006; Bowman et al., 2015; Williams et al., 2018), but we instead measure whether models assign a higher likelihood to valid statements of entailment compared to minimally differing invalid statements.”
Anderson, A. J., Kiela, D., Binder, J. R., Fernandino, L., Humphries, C. J., Conant, L. L., Raizada, R. D. S., Grimm, S., & Lalor, E. C. (2021) · The Journal of Neuroscience · doi:10.1523/jneurosci.1152-20.2021 · Cited by 70
Hagendorff, T., Dasgupta, I., Binz, M., Chan, S. C. Y., Lampinen, A., Wang, J. X., Akata, Z., & Schulz, E. (2023) · arXiv · doi:10.48550/arxiv.2303.13988 · Cited by 68
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
Kayser, M., Camburu, O.-M., Salewski, L., Emde, C., Do, V., Akata, Z., & Lukasiewicz, T. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00128 · Cited by 68
Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.14249 · Cited by 65
On the Paradox of Learning to Reason from Data
Zhang, H., Li, L. H., Meng, T., Chang, K.-W., & Broeck, G. V. D. (2022) · arXiv · doi:10.48550/arxiv.2205.11502 · Cited by 58
Erdem, E., Kuyu, M., Yagcioglu, S., Frank, A., Parcalabescu, L., Plank, B., Babii, A., Turuta, O., Erdem, A., Calixto, I., et al. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12918 · Cited by 54
TRUE: Re-evaluating Factual Consistency Evaluation
Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (2022) · Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering · doi:10.18653/v1/2022.dialdoc-1.19 · Cited by 56
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
Ben-David, E., Oved, N., & Reichart, R. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00468 · Cited by 54
Langer, M., Hunsicker, T., Feldkamp, T., König, C. J., & Grgić-Hlača, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517527 · Cited by 55
Discovering Language Model Behaviors with Model-Written Evaluations
Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.09251 · Cited by 53
Self-citationTesting AI on language comprehension tasks reveals insensitivity to underlying meaning
Dentella, V., Günther, F., Murphy, E., Marcus, G., & Leivada, E. (2024) · Scientific Reports · doi:10.1038/s41598-024-79531-8 · Cited by 54
Contrastive Learning Models for Sentence Representations
Xu, L., Xie, H., Li, Z., Wang, F. L., Wang, W., & Li, Q. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3593590 · Cited by 50
On the Paradox of Learning to Reason from Data
Zhang, H., Li, L. H., Meng, T., Chang, K.-W., & Van den Broeck, G. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/375 · Cited by 58
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Roller, S., Boureau, Y.-L., Weston, J., Bordes, A., Dinan, E., Fan, A., Gunning, D., Ju, D., Li, M., Poff, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.12442 · Cited by 38
Metadata
- Authors
- Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Published
- 2015
- Pages
- 632-642
- OpenAlex
- W1840435438
- Cited by
- 3,377
- References
- 30
- Citations by year
- 2026: 89 · 2025: 172 · 2024: 219 · 2023: 564 · 2022: 488 · 2021: 596 · 2020: 499 · 2019: 390 · 2018: 227 · 2017: 87 · 2016: 45 · 2015: 1
- FWCI
- 67.61
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1159679)
- Defense Advanced Research Projects Agency (FA8750)
- Advanced Research Projects Agency
- U.S. Navy
- Office of Naval Research (N00014)
- Air Force Research Laboratory
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- natural language inference, logical consequence, contradiction, annotated corpus, sentence pairs, image captioning, neural network model, semantic representation
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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