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 to determine if a summary contradicts a context or not.
“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
InferSent uses labeled data of the Stanford Natural Language Inference dataset and the MultiGenre NLI dataset to train a siamese BiLSTM network with max-pooling over the output.
“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
Most prior entailment datasets have been human-authored, which is known to introduce artifacts, or collected in the wild, which is challenging to scale.
“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
This released mixture uses SNLI, MNLI, FEVER, SciTail, PAWS, and VitaminC rather than the original TRUE paper's ANLI recipe.
“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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Measuring the Carbon Intensity of AI in Cloud Instances
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Dialogue Natural Language Inference
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WARP: Word-level Adversarial ReProgramming
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<i>Did Aristotle Use a Laptop?</i> A Question Answering Benchmark with Implicit Reasoning Strategies
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TabFact: A Large-scale Dataset for Table-based Fact Verification
Chen, W., Wang, H., Chen, J., Zhang, Y., Wang, H., Li, S., Zhou, X., & Wang, W. Y. (2019) · arXiv · doi:10.48550/arxiv.1909.02164 · Cited by 239 · see citing works
<scp>SummaC</scp>: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
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Cloze-driven Pretraining of Self-attention Networks
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Large Language Models Can Self-Improve
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Measuring and improving consistency in pretrained language models
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Movement pruning: Adaptive sparsity by fine-tuning
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Inherent Disagreements in Human Textual Inferences
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Calibration of Pre-trained Transformers
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A Survey of Evaluation Metrics Used for NLG Systems
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Large Language Models Can Self-Improve
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Enabling Large Language Models to Generate Text with Citations
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Muppet: Massive Multi-task Representations with Pre-Finetuning
Aghajanyan, A., Gupta, A., Shrivastava, A., Chen, X., Zettlemoyer, L., & Gupta, S. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.468 · Cited by 183 · see citing works
How many data points is a prompt worth?
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MetaICL: Learning to Learn In Context
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1070 · Cited by 2,049 · see citing works
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
Schick, T., Schmid, H., & Schütze, H. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.488 · Cited by 184 · see citing works
Feng, S., Park, C. Y., Liu, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.656 · Cited by 149 · see citing works
How Can We Accelerate Progress Towards Human-like Linguistic Generalization?
Linzen, T. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.465 · Cited by 148 · see citing works
RARR: Researching and revising what language models say, using language models
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Structured Pruning Learns Compact and Accurate Models
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“(Williams et al., 2018) development sets.”
Enhancing Factual Consistency of Abstractive Summarization
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Generating Datasets with Pretrained Language Models
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QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
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SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
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Thieves on Sesame Street! Model Extraction of BERT-based APIs
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Language Model Behavior: A Comprehensive Survey
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Differentially Private Fine-tuning of Language Models
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Evaluating the logical reasoning ability of chatgpt and GPT-4
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On the Robustness of Self-Attentive Models
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Bad Characters: Imperceptible NLP Attacks
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With Little Power Comes Great Responsibility
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Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
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Contrastive Explanations for Model Interpretability
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Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
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e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
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EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy
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- 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
- 3
- Citations by year
- 2026: 94 · 2025: 205 · 2024: 248 · 2023: 651 · 2022: 542 · 2021: 710 · 2020: 471 · 2019: 282 · 2018: 118 · 2017: 21
- 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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