Annotation Artifacts in Natural Language Inference Data
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Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, Noah A. Smith. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.
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An NLP expert wrote negations of existing claims, taking precautions not to bias the negations by using obvious keywords like 'not'.
“To obtain examples where an abstract REFUTES a claim, an NLP expert wrote negations of existing claims, taking precautions not to bias the negations by using obvious keywords like 'not' (Schuster et al., 2019; Gururangan et al., 2018).”
From Fact or Fiction: Verifying Scientific Claims (2020) · cites this paper as Gururangan et al., 2018
The Introduction and the Entailment section report that negation words are strong cues for contradiction, supporting the concern about keyword bias. They do not say that an NLP expert wrote negations of existing claims or took precautions to avoid such keywords when creating REFUTES examples. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDeep Neural Networks have been proven to easily learn biases from the data, which significantly impacts their reliability.
“Deep Neural Networks have been proven to easily learn biases from the data, which significantly impacts their reliability.”
From Large Language Models are not Fair Evaluators (2024) · cites this paper as Gururangan et al., 2018
Section 4, “Re-evaluating NLI Models,” finds that high-performing NLI models rely heavily on annotation artifacts and perform substantially worse on harder examples, calling their apparent ability into question. This supports the point for the NLI models studied, but not the broader claim about deep neural networks generally. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMost 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 Gururangan et al., 2018
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- Authors
- Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., & Smith, N. A.
- Affiliations
- University of Washington (US)
- Carnegie Mellon University (US)
- Google (United States) (US)
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Published
- 2018
- Pages
- 107-112
- OpenAlex
- W2790415926
- Cited by
- 907
- References
- 1
- Citations by year
- 2026: 23 · 2025: 25 · 2024: 44 · 2023: 148 · 2022: 136 · 2021: 199 · 2020: 171 · 2019: 117 · 2018: 42 · 2017: 2
- FWCI
- 53.99
- 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) (cc-by)
- Funders
- Nvidia
- Tencent
- Defense Advanced Research Projects Agency
- Samsung
- Army Research Office (W911NF-15-1-, W911NF, W911NF-15)
- 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
- annotation artifacts, natural language inference, annotated dataset, manual annotation, annotation errors
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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