# HellaSwag 2025

HellaSwag 2025 is an updated benchmark for evaluating common-sense reasoning in AI models, introduced in 2025 by a consortium of academic labs, featuring harder adversarial examples and a focus on multimodal understanding.

HellaSwag 2025 is a benchmark dataset designed to evaluate the common-sense reasoning capabilities of artificial intelligence systems, particularly [large language models](https://www.wikiprompt.org/wiki/large-language-model) and multimodal models. It is the successor to the original HellaSwag benchmark, which was introduced in 2019 by researchers at the [University of Toronto](https://www.wikiprompt.org/wiki/university-of-toronto) and the Allen Institute for AI. The 2025 version updates the dataset with more challenging examples, expanded coverage of visual and audio scenarios, and a stricter evaluation protocol to reduce inflated scores from memorization or statistical shortcuts.

The benchmark tests a model's ability to choose the most plausible continuation of a given scenario, requiring an understanding of physical dynamics, social conventions, and temporal causality. Unlike earlier versions that focused primarily on text, HellaSwag 2025 includes multimodal prompts that combine text with images or audio clips, pushing models to integrate information across modalities. The dataset is constructed using a combination of human-written scenarios and AI-generated adversarial examples, filtered to ensure quality and difficulty.

## Background and Motivation

The original HellaSwag was created to address a gap in existing benchmarks, which often failed to distinguish between models that truly understand common sense and those that rely on superficial linguistic patterns. The name is a portmanteau of "hell" and "swag" (a playful reference to "silly wisdom" or "common sense"). The 2025 edition was developed in response to the rapid advancement of generative AI, which has led to models that can achieve near-perfect scores on older benchmarks, making it difficult to measure further progress.

The benchmark is maintained by a consortium of academic and industry researchers, including members from [Stanford AI Lab](https://www.wikiprompt.org/wiki/stanford-ai-lab), [MIT CSAIL](https://www.wikiprompt.org/wiki/mit-csail), and [Berkeley AI Research](https://www.wikiprompt.org/wiki/berkeley-ai-research). The project receives funding from multiple sources, including the National Science Foundation and private foundations.

## Dataset Construction

HellaSwag 2025 contains approximately 20,000 multiple-choice questions, each with a context and four possible endings, only one of which is correct. The contexts are drawn from a diverse range of sources, including movie scripts, instructional videos, and everyday scenarios. To increase difficulty, the dataset includes "adversarial" examples generated by AI systems, which are then validated by human annotators to ensure they are solvable by humans but challenging for machines.

The construction process involves several steps: first, a large pool of candidate scenarios is collected; second, AI models generate plausible and implausible endings; third, human annotators filter and label the examples; finally, a calibration set is used to ensure that the benchmark is not biased by trivial cues such as length or word frequency. The 2025 version also introduces a "counterfactual" subset, where the correct answer requires reasoning about hypothetical changes to the scenario.

## Evaluation and Scoring

Models are evaluated by their accuracy on the test set, with the primary metric being the percentage of correctly answered questions. To prevent overfitting, the test set is not publicly released; instead, researchers submit their models for evaluation through a controlled API, similar to the approach used by other benchmarks like [OpenAI](https://www.wikiprompt.org/wiki/openai)'s evals. The benchmark also reports a "robustness score" that measures performance on perturbed versions of the questions, such as paraphrased contexts or altered images.

HellaSwag 2025 is designed to be more difficult than its predecessor. In initial evaluations, state-of-the-art models such as [GPT-4](https://www.wikiprompt.org/wiki/gpt-4) and [Claude](https://www.wikiprompt.org/wiki/claude) achieve around 85% accuracy, compared to 95% on the original HellaSwag. This gap indicates that the new benchmark provides a better signal for distinguishing between models with different levels of reasoning ability.

## Impact and Reception

The release of HellaSwag 2025 has been met with interest from the AI research community. Many researchers use it as a standard evaluation tool when developing new architectures or training methods. The benchmark has also been adopted by industry labs, including [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind) and [Anthropic](https://www.wikiprompt.org/wiki/anthropic), as part of their internal evaluation suites. Some critics argue that the benchmark still has limitations, such as a potential bias toward Western cultural contexts, but the consortium has made efforts to include diverse scenarios.

The benchmark has also influenced the development of new training techniques, such as [curriculum learning](https://www.wikiprompt.org/wiki/curriculum-learning) and [data augmentation](https://www.wikiprompt.org/wiki/data-augmentation), which aim to improve common-sense reasoning. In addition, HellaSwag 2025 has been used to study the phenomenon of "shortcut learning," where models exploit statistical regularities rather than true understanding.

## Future Directions

Plans for future versions of HellaSwag include expanding to more languages, incorporating interactive or embodied scenarios, and adding a temporal reasoning component. The consortium also intends to release a leaderboard that tracks progress over time, similar to the SuperGLUE benchmark. As AI systems continue to evolve, benchmarks like HellaSwag 2025 play a crucial role in ensuring that progress is measured in meaningful ways.

## See Also

- [Machine learning](https://www.wikiprompt.org/wiki/machine-learning)
- [Deep learning](https://www.wikiprompt.org/wiki/deep-learning)
- [Neural network](https://www.wikiprompt.org/wiki/neural-network)
- [Transformer](https://www.wikiprompt.org/wiki/transformer)
- [Generative AI](https://www.wikiprompt.org/wiki/generative-ai)

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Source: https://www.wikiprompt.org/wiki/hellaswag-2025
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-13T03:54:25.303423+00:00