2021/09/28 by Sean Welleck, Welleck, Sean, Peter West +5 · 3 citations
Computer Science · Materials Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Model Reduction and Neural Networks #Neural Networks and Applications #cs.LG
paper · pdf · doi:10.48550/arxiv.2109.13986
AAAI 2022
openalex publication_date 2021/09/28 · arxiv created 2022/02/24 · arxiv updated 2022/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance. However, their ability to achieve stronger forms of generalization remains unclear. We consider the problem of symbolic mathematical integration, as it requires generalizing systematically beyond the test set. We develop a methodology for evaluating generalization that takes advantage of the problem domain's structure and access to a verifier. Despite promising in-distribution performance of sequence-to-sequence models in this domain, we demonstrate challenges in achieving robustness, compositionality, and out-of-distribution generalization, through both carefully constructed manual test suites and a genetic algorithm that automatically finds large collections of failures in a controllable manner. Our investigation highlights the difficulty of generalizing well with the predominant modeling and learning approach, and the importance of evaluating beyond the test set, across different aspects of generalization.