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HellaSwag: Can a Machine Really Finish Your Sentence?
Open accessAbstract
Recent work by Zellers et al. (2018) introduced a new task of commonsense natural language inference: given an event description such as "A woman sits at a piano," a machine must select the most likely followup: "She sets her fingers on the keys."With the introduction of BERT (Devlin et al., 2018), near human-level performance was reached.Does this mean that machines can perform human level commonsense inference?In this paper, we show that commonsense inference still proves di cult for even stateof-the-art models, by presenting HellaSwag, a new challenge dataset.Though its questions are trivial for humans (°95% accuracy), state-of-the-art models struggle ( †48%).We achieve this via Adversarial Filtering (AF), a data collection paradigm wherein a series of discriminators iteratively select an adversarial set of machine-generated wrong answers.AF proves to be surprisingly robust.The key insight is to scale up the length and complexity of the dataset examples towards a critical 'Goldilocks' zone wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models.Our construction of HellaSwag, and its resulting di culty, sheds light on the inner workings of deep pretrained models.More broadly, it suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges.
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“There is no 'ground-truth' implementation from the original benchmark authors, and many popular benchmarks (Paperno et al., 2016; Clark et al., 2018; Zellers et al., 2019) that have been 'retrofitted' for compatibility with autoregressive LMs.”
From Lessons from the Trenches on Reproducible Evaluation of Language Models (2024) · cites this paper as Zellers et al., 2019
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“Second, benchmarks are often adversarially selected for tasks that current models struggle with compared to humans, 1 biasing the comparison to human performance.”
From Measuring AI Ability to Complete Long Software Tasks (2025) · cites this paper as [4]
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“During RLHF fine-tuning, we observe performance regressions compared to GPT-3 on certain public NLP datasets, notably SQuAD (Rajpurkar et al., 2018), DROP (Dua et al., 2019), HellaSwag (Zellers et al., 2019), and WMT 2015 French to English translation (Bojar et al., 2015).”
From Training language models to follow instructions with human feedback (2022) · cites this paper as Zellers et al., 2019
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“Traditional language model benchmarks, such as HellaSwag (Zellers et al., 2019), WinoGrande (Sakaguchi et al., 2021), MMLU (Hendrycks et al., 2021), GSM-8K (Cobbe et al., 2021), and BIG-Bench (Srivastava et al., 2023), primarily assess LLMs on tasks like commonsense reasoning and standardized exams.”
From LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints (2024) · cites this paper as Zellers et al., 2019
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“for evaluating the following tasks that measure common sense reasoning: LAMBADA (OpenAI version in LM Evaluation Harness) (Paperno et al., 2016), HellaSwag (Zellers et al., 2019), PIQA (Bisk et al., 2020), ARC-challenge, ARC-easy (Clark et al., 2018), WinoGrande (Sakaguchi et al., 2021).”
From xLSTM: Extended Long Short-Term Memory (2024) · cites this paper as Zellers et al., 2019
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Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
Utama, P. A., Moosavi, N. S., & Gurevych, I. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.770 · Cited by 106
xLSTM: Extended Long Short-Term Memory
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Adversarial Training for Large Neural Language ModelsPreprint
Liu, X., Cheng, H., He, P., Chen, W., Wang, Y., Poon, H., & Gao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.08994 · Cited by 208
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Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
Lu, J., Clark, C., Lee, S.-H., Zhang, Z., Khosla, S., Marten, R., Hoiem, D., & Kembhavi, A. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02497 · Cited by 195
Research community dynamics behind popular AI benchmarks
Martínez-Plumed, F., Barredo, P., hÉigeartaigh, S. Ó., & Hernández-Orallo, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00339-6 · Cited by 86
Efficient Large Scale Language Modeling with Mixtures of Experts
Artetxe, M., Bhosale, S., Goyal, N., Mihaylov, T., Ott, M., Shleifer, S., Lin, X. V., Du, J., Iyer, S. V., Pasunuru, R., et al. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.804 · Cited by 223
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition
Jeretič, P., Warstadt, A., Bhooshan, S., & Williams, A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.768 · Cited by 101
MAGE: Machine-generated Text Detection in the Wild
Li, Y., Li, Q., Cui, L., Bi, W., Wang, Z., Wang, L., Yang, L., Shi, S., & Zhang, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.3 · Cited by 79
Benchmarks for Automated Commonsense Reasoning: A Survey
Davis, E. (2023) · ACM Computing Surveys · doi:10.1145/3615355 · Cited by 101
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningPreprint
Aribandi, V., Tay, Y., Schuster, T., Rao, J., Zheng, H., Mehta, S. V., Zhuang, H., Tran, V. Q., Bahri, D., Ni, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10952 · Cited by 132
Understanding Emergent Abilities of Language Models from the Loss Perspective
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Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
Ma, K., Ilievski, F., Francis, J., Bisk, Y., Nyberg, E., & Oltramari, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17593 · Cited by 79
Self-citationWhat to Pre-Train on? Efficient Intermediate Task Selection
Poth, C., Pfeiffer, J., Rücklé, A., & Gurevych, I. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.827 · Cited by 92
Delphi: Towards Machine Ethics and NormsPreprint
Jiang, L., Hwang, J. D., Bhagavatula, C., Bras, R. J. L., Forbes, M., Borchardt, J., Liang, J., Etzioni, O., Sap, M., & Choi, Y. (2021) · arXiv (Cornell University) · Cited by 74
Self-citationLarge language models for life cycle assessments: Opportunities, challenges, and risks
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Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
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Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets
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jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
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Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction
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Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning
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Natural Language Reasoning, A Survey
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DynaSent: A Dynamic Benchmark for Sentiment Analysis
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MaScQA: investigating materials science knowledge of large language models
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TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
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The role of large language models in agriculture: harvesting the future with LLM intelligence
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Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
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“The structured nature of these benchmarks allows for clear evaluation of the model’s confidence in its predictions.”
Testing AI on language comprehension tasks reveals insensitivity to underlying meaning
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FashionGPT: LLM instruction fine-tuning with multiple LoRA-adapter fusion
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The Curious Case of Commonsense Intelligence
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Self-citationA Systematic Investigation of Commonsense Knowledge in Large Language Models
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Contextual feature extraction hierarchies converge in large language models and the brain
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SCOTT: Self-Consistent Chain-of-Thought Distillation
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Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation
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Scaling Data-Constrained Language Models
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Systematic Biases in LLM Simulations of Debates
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“For example, we argue that our findings can inspire people to use these tools to infer and remove biases from existing models.”
Metadata
- Authors
- Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y.
- Affiliations
- University of Washington (US)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 4791-4800
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2946609015
- Cited by
- 830
- References
- 20
- Citations by year
- 2026: 200 · 2025: 263 · 2024: 109 · 2023: 93 · 2022: 50 · 2021: 61 · 2020: 44 · 2019: 10
- FWCI
- 15.78
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Science Foundation (1703166, 1256082, 1637479, 1524371)
- Nvidia
- Allen Institute for Artificial Intelligence
- Defense Advanced Research Projects Agency
- Intelligence Advanced Research Projects Activity
- Army Research Office (W911NF-15-1-, W911NF)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- commonsense reasoning, natural language inference, AI-generated text, BERT, benchmark design, pre-trained language models
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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