%0 Conference Proceedings
%T LILA: A Unified Benchmark for Mathematical Reasoning
%A Mishra, Swaroop
%A Finlayson, Matthew
%A Lu, Pan
%A Tang, Leonard
%A Welleck, Sean
%A Baral, Chitta
%A Rajpurohit, Tanmay
%A Tafjord, Oyvind
%A Sabharwal, Ashish
%A Clark, Peter
%A Kalyan, Ashwin
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F mishra-etal-2022-lila
%X Mathematical reasoning skills are essential for general-purpose intelligentsystems to perform tasks from grocery shopping to climate modeling.Towards evaluating and improving AI systems in this domain, we proposeLILA, a unified mathematical reasoning benchmark consisting of 23 diversetasks along four dimensions:(i) mathematical abilities e.g., arithmetic, calculus (ii) language format e.g., question-answering, fill-in-the-blanks (iii) language diversity e.g., no language, simple language (iv) external knowledge e.g., commonsense, physics. We construct our benchmark by extending 20 datasets benchmark by collecting task instructions and solutions in the form of Python programs,thereby obtaining explainable solutions in addition to the correct answer.We additionally introduce two evaluation datasets to measure out-of-distribution performance and robustness to language perturbation.Finally, we introduce BHASKARA,a general-purpose mathematical reasoning model trained on LILA. Importantly, we find that multi-tasking leads to significant improvements (average relative improvement of 21.83% F1 score vs. single-task models),while the best performing model only obtains 60.40%,indicating the room for improvement in general mathematical reasoning and understanding.
%R 10.18653/v1/2022.emnlp-main.392
%U https://aclanthology.org/2022.emnlp-main.392
%U https://doi.org/10.18653/v1/2022.emnlp-main.392
%P 5807-5832