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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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Kaushik, D., Kiela, D., Lipton, Z. C., & Yih, W.-T. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.517 · Cited by 30
Harder Task Needs More Experts: Dynamic Routing in MoE Models
Huang, Q., An, Z., Zhuang, N., Tao, M., Zhang, C., Jin, Y., Xu, K., Xu, K., Chen, L., Huang, S., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.696 · Cited by 23
A Theoretically Grounded Question Answering Data Set for Evaluating Machine Common Sense
Santos, H., Shen, K., Mulvehill, A. M., Kejriwal, M., & McGuinness, D. L. (2023) · Data Intelligence · doi:10.1162/dint_a_00234 · Cited by 21
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-Based Large Language Models
Ning, M., Zhu, B., Xie, Y., Lin, B., Cui, J., Yuan, L., Chen, D., & Yuan, L. (2025) · Computational Visual Media · doi:10.26599/cvm.2025.9450516 · Cited by 121
Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers
Phang, J., Liu, H., & Bowman, S. R. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.42 · Cited by 24
Pre-Training to Learn in Context
Gu, Y., Dong, L., Wei, F., & Huang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.267 · Cited by 51
A reliable knowledge processing framework for combustion science using foundation models
Sharma, V., & Raman, V. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100365 · Cited by 24
Transfer learning for language model adaptation
Saiful, B. M. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/169892 · Cited by 22
A noise audit of human-labeled benchmarks for machine commonsense reasoning
Kejriwal, M., Santos, H., Shen, K., Mulvehill, A. M., & McGuinness, D. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-58937-4 · Cited by 21
EuroLLM: Multilingual Language Models for Europe
Martins, P. H., Fernandes, P., Alves, J., Guerreiro, N. M., Rei, R., Alves, D. M., Pombal, J., Farajian, M. A., Faysse, M., Klimaszewski, M., et al. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.02.260 · Cited by 151
Rethinking Why Intermediate-Task Fine-Tuning Works
Chang, T.-Y., & Lu, C.-J. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.61 · Cited by 30
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Utama, P. A., Moosavi, N. S., Sanh, V., & Gurevych, I. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.713 · Cited by 23
LangTest: A comprehensive evaluation library for custom LLM and NLP models
Nazir, A., Chakravarthy, T. K., Cecchini, D., Khajuria, R., Sharma, P., Mirik, A. T., Kocaman, V., & Talby, D. (2024) · Software Impacts · doi:10.1016/j.simpa.2024.100619 · Cited by 22
Natural Language Processing with Commonsense Knowledge: A SurveyPreprint
Xie, Y., Liu, Z., Ma, Z., Meng, F., Xiao, Y., Miao, F., & Pu, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.04674 · Cited by 25
Nezhurina, M., Cipolina-Kun, L., Cherti, M., & Jitsev, J. (2024) · arXiv · doi:10.48550/arxiv.2406.02061 · Cited by 21
“We again observe a failure of the majority of the models to cope with this generic form, being unable to produce a correct generic solution, e.g. [29], PIQA [30], GSM8K [31], HellaSwag [32], MMLU [33] or WinoGrande [34].”
BackgroundMedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain
Herlihy, C. P., & Rudinger, R. (2021) · doi:10.18653/v1/2021.acl-short.129 · Cited by 30
Zafar, A., Parthasarathy, V. B., Van, C. L., Shahid, S., Khan, A. I., & Shahid, A. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8060070 · Cited by 30
CIPHER: Cybersecurity Intelligent Penetration-Testing Helper for Ethical Researcher
Pratama, D., Suryanto, N., Adiputra, A. A., Le, T.-T.-H., Kadiptya, A. Y., Iqbal, M. J., & Kim, H. (2024) · Sensors · doi:10.3390/s24216878 · Cited by 34
Efficient LLM Inference on CPUs
Shen, H., Chang, H., Dong, B., Luo, Y., & Meng, H. (2025) · Machine translation · doi:10.1007/978-3-031-85747-8_3 · Cited by 55
jiant: A Software Toolkit for Research on General-Purpose Text Understanding ModelsPreprint
Pruksachatkun, Y., Yeres, P., Liu, H., Phang, J., Htut, P. M., Wang, A., Tenney, I. F., & Bowman, S. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.02249 · Cited by 27
So you want your private LLM at home? A survey and benchmark of methods for efficient GPTs
Tuggener, L., Sager, P., Taoudi-Benchekroun, Y., Grewe, B. F., & Stadelmann, T. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00036 · Cited by 23
A hybrid model based on transformer and Mamba for enhanced sequence modeling
Zhu, X., Ruan, Q., Qian, S., & Zhang, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-87574-8 · Cited by 26
LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference
Mo, Z., Wang, L., Wei, J., Zeng, Z., Cao, S., Ma, L., Jing, N., Cao, T., Xue, J., Yang, F., et al. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731057 · Cited by 39
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
Huang, Z., Wu, A., Zhou, J., Gu, Y., Zhao, Y., & Cheng, G. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.239 · Cited by 19
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
Ye, Q., Yu, Z., Shao, R., Xie, X., Torr, P. H. S., & Cao, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72684-2_9 · Cited by 37
Tucano: Advancing neural text generation for Portuguese
Corrêa, N. K., Sen, A., Falk, S., & Fatimah, S. (2025) · Patterns · doi:10.1016/j.patter.2025.101325 · Cited by 36
“The remaining four benchmarks, ARC-Challenge [31], HellaSwag [179], and TruthfulQA [97] are all evaluations tied to a machine-translated version of the original (English) datasets, made available by a multilingual implementation of the Language Model Evaluation Harness (Lai et al. [88]).”
Who's Harry Potter? Approximate Unlearning in LLMs
Eldan, R., & Russinovich, M. (2023) · arXiv · doi:10.48550/arxiv.2310.02238 · Cited by 19
Titans: Learning to Memorize at Test Time
Behrouz, A., Zhong, P., & Mirrokni, V. (2025) · arXiv · doi:10.48550/arxiv.2501.00663 · Cited by 24
“Following recent studies on linear recurrent models [35, 30, 38], we use Wikitext [123], LMB [124], PIQA [125], HellaSwag [126], WinoGrande [127], ARC-easy (ARC-e) and ARC-challenge (ARC-c) [128], SIQA [129], and BoolQ [130].”
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization
Kim, M., Hong, S., Ko, R., Choi, S., Lee, H., Kim, J., Kim, J.-Y., & Park, J. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731019 · Cited by 25
“Wikitext2 [46] dataset consists of tokens extracted from Wikipedia articles, while PIQA [7], Wino-grande [58], and Hellaswag [81] constitute questions and answers.”
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations
Schwartz, R., & Stanovsky, G. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.168 · Cited by 23
Wang, X., Huang, R., Jin, Z., Fang, T., & Qu, H. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3327153 · Cited by 21
How to Regulate Large Language Models for Responsible AI
Berengueres, J. O. L. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3403681 · Cited by 24
“This measure tests a text model’s multitask accuracy across 57 tasks, including elementary mathematics, U.S. history, computer science, law, and more [56].”
BackgroundMethodComparing Test Sets with Item Response Theory
Vania, C., Htut, P. M., Huang, W., Mungra, D., Pang, R. Y., Phang, J., Liu, H., Cho, K., & Bowman, S. R. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.92 · Cited by 18
Evaluating Document Coherence Modeling
Shen, A., Mistica, M., Salehi, B., Li, H., Baldwin, T., & Qi, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00388 · Cited by 22
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization
Akula, A., Changpinyo, S., Gong, B., Sharma, P. K., Zhu, S. C., & Soricut, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.164 · Cited by 19
A Survey of Reasoning with Foundation ModelsPreprint
Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Liu, J., Xu, J., Ding, M., Li, H., Geng, M., et al. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/ac4sp · Cited by 143
Self-Rewarding Language Models
Yuan, W., Pang, R. Y., Cho, K., Li, X., Sukhbaatar, S., Xu, J., & Weston, J. (2024) · arXiv · doi:10.48550/arxiv.2401.10020 · Cited by 18
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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