TurkuNLP/finbenchv2-goldenswag-fi-ht

This is a machine-translated and manually corrected subset of GoldenSwag used in Finbench version 2.

Citation information

To cite the original GoldenSwag work:

@misc{chizhov2025hellaswagvaliditycommonsensereasoning,
      title={What the HellaSwag? On the Validity of Common-Sense Reasoning Benchmarks}, 
      author={Pavel Chizhov and Mattia Nee and Pierre-Carl Langlais and Ivan P. Yamshchikov},
      year={2025},
      eprint={2504.07825},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.07825}, 
}

To cite the original HellaSwag dataset:

@inproceedings{zellers-etal-2019-hellaswag,
    title = "{H}ella{S}wag: Can a Machine Really Finish Your Sentence?",
    author = "Zellers, Rowan  and
      Holtzman, Ari  and
      Bisk, Yonatan  and
      Farhadi, Ali  and
      Choi, Yejin",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1472/",
    doi = "10.18653/v1/P19-1472",
    pages = "4791--4800",
    abstract = "Recent work by Zellers et al. (2018) introduced a new task of commonsense natural language inference: given an event description such as {\textquotedblleft}A woman sits at a piano,{\textquotedblright} a machine must select the most likely followup: {\textquotedblleft}She sets her fingers on the keys.{\textquotedblright} With the introduction of BERT, 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 difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Though its questions are trivial for humans ({\ensuremath{>}}95{\%} accuracy), state-of-the-art models struggle ({\ensuremath{<}}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 {\textquoteleft}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 difficulty, 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."
}

Licensing information

MIT