# HellaSwag HellaSwag is a benchmark dataset for evaluating commonsense reasoning in AI models, consisting of multiple-choice questions that require predicting the most plausible ending to a given scenario. HellaSwag is a benchmark dataset designed to evaluate the commonsense reasoning capabilities of artificial intelligence systems, particularly large language models. It was introduced in 2019 by Rowan Zellers and colleagues at the University of Washington and the Allen Institute for AI. The name is a portmanteau of "hell" and "swag," where "swag" stands for "Situations With Adversarial Generations." The benchmark challenges models to select the most plausible continuation of a given narrative from a set of options, testing their ability to understand everyday physical and social situations. The dataset comprises over 70,000 multiple-choice questions, each derived from video captions and story descriptions. The questions are designed to be adversarial, meaning the incorrect answer choices are generated by a language model to be superficially plausible but semantically wrong. This design forces models to rely on genuine commonsense understanding rather than surface-level linguistic patterns or statistical shortcuts. ## Construction and Design The construction of HellaSwag involves a two-stage process. First, the authors collected a large corpus of narrative descriptions from video captioning datasets such as ActivityNet and WikiHow. These descriptions provide the context for each question. Second, they used a generative language model to produce candidate endings for each context. The model-generated endings, which are often grammatically correct but logically inconsistent with the scenario, serve as the distractor options. The correct ending is the original continuation from the source corpus. This adversarial generation process is a key feature of HellaSwag. It ensures that the benchmark is not easily gamed by models that rely on lexical overlap or simple heuristics. The authors demonstrated that many existing models, including those with strong performance on other benchmarks, performed poorly on HellaSwag, achieving accuracy only slightly above random guessing. ## Evaluation and Impact HellaSwag has become a standard evaluation tool in the field of natural language processing. It is included in major evaluation suites such as the Open LLM Leaderboard and the EleutherAI Language Model Evaluation Harness. Researchers use it to compare the reasoning abilities of different models, including those based on the [transformer](https://www.wikiprompt.org/wiki/transformer) architecture and [large language models](https://www.wikiprompt.org/wiki/large-language-model). Initial results on HellaSwag highlighted significant gaps in machine commonsense reasoning. For instance, in the original paper, the best-performing model at the time achieved an accuracy of around 48%, compared to a human performance of approximately 94%. This stark contrast underscored the limitations of early [deep learning](https://www.wikiprompt.org/wiki/deep-learning) systems in understanding nuanced real-world scenarios. Subsequent improvements in model architecture and training data have led to substantial gains. Modern large language models, such as those developed by [openai](https://www.wikiprompt.org/wiki/openai), [anthropic](https://www.wikiprompt.org/wiki/anthropic), and [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind), now achieve accuracy above 90% on HellaSwag. This progress reflects broader advances in [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) and [machine-learning](https://www.wikiprompt.org/wiki/machine-learning), particularly in the ability of models to capture and apply commonsense knowledge. ## Limitations and Criticisms Despite its widespread use, HellaSwag has faced criticism. Some researchers argue that the benchmark may overestimate the reasoning abilities of models because it relies on multiple-choice format, which can be solved through pattern matching or memorization of similar examples in training data. The adversarial distractors, while initially effective, may become less challenging as models are trained on larger and more diverse corpora that include similar question formats. Additionally, the benchmark primarily tests physical and social commonsense in English, which limits its applicability to other languages and cultural contexts. Efforts to create multilingual versions, such as the X-CSQA and XCOPA datasets, have attempted to address this gap, but HellaSwag itself remains an English-only resource. ## Related Benchmarks HellaSwag is part of a family of commonsense reasoning benchmarks that include SWAG (its predecessor), COPA, and Winograd Schema Challenge. These benchmarks collectively aim to measure different aspects of commonsense understanding, from physical intuition to social dynamics. They are often used together in model evaluation to provide a comprehensive assessment of reasoning capabilities. The development of HellaSwag also influenced the creation of other adversarial benchmarks, such as ANLI (Adversarial NLI) and TruthfulQA, which apply similar generation techniques to test robustness and factuality. These benchmarks have become integral to the iterative improvement of AI systems, guiding research in areas like [neural networks](https://www.wikiprompt.org/wiki/neural-network) and [generative AI](https://www.wikiprompt.org/wiki/generative-ai). ## See Also - [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) - [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) - [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) - [transformer](https://www.wikiprompt.org/wiki/transformer) ## References - Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019). HellaSwag: Can a Machine Really Finish Your Sentence? Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. --- Source: https://www.wikiprompt.org/wiki/hellaswag License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/) Last updated: 2026-09-08T06:10:43.969371+00:00