| # HellaSwag | |
| ### Paper | |
| Title: `HellaSwag: Can a Machine Really Finish Your Sentence?` | |
| Abstract: https://arxiv.org/abs/1905.07830 | |
| 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, 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 (>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 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. | |
| Homepage: `https://rowanzellers.com/hellaswag/` | |
| ### Citation | |
| ``` | |
| @inproceedings{zellers2019hellaswag, | |
| title={HellaSwag: Can a Machine Really Finish Your Sentence?}, | |
| author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin}, | |
| booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, | |
| year={2019} | |
| } | |
| ``` | |
| ### Groups and Tasks | |
| #### Groups | |
| - Not part of a group yet | |
| #### Tasks | |
| - `hellaswag` | |
| ### Checklist | |
| For adding novel benchmarks/datasets to the library: | |
| * [x] Is the task an existing benchmark in the literature? | |
| * [x] Have you referenced the original paper that introduced the task? | |
| * [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test? | |
| If other tasks on this dataset are already supported: | |
| * [ ] Is the "Main" variant of this task clearly denoted? | |
| * [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates? | |
| * [ ] Have you noted which, if any, published evaluation setups are matched by this variant? | |