Year: 2021
Type: preprint
Abstract: State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning. To diagnose the failures of current models and support research, we introduce GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems. We find that even the largest transformer models fail to achieve high test performance, despite the conceptual simplicity of this problem distribution. To increase performance, we propose training verifiers to judge the correctness of model completions. At test time, we generate many candidate solutions and select the one ranked highest by the verifier. We demonstrate that verification significantly improves performance on GSM8K, and we provide strong empirical evidence that verification scales more effectively with increased data than a finetuning baseline.
Source: arXiv (Cornell University)
Authors: Cobbe, Karl, Kosaraju, Vineet, Bavarian, Mohammad, Chen, Mark, Jun, Heewoo, Kaiser, Lukasz, Plappert, Matthias, Tworek, Jerry, Hilton, Jacob, Nakano, Reiichiro, Hesse, Christopher, Schulman, John
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W3210904070