{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T17:40:15Z","timestamp":1756489215512,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,24]]},"DOI":"10.1145\/3674029.3674041","type":"proceedings-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T12:25:22Z","timestamp":1726057522000},"page":"74-78","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Figuring Figures: An assessment of large language models on different modalities of math word problems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4673-7967","authenticated-orcid":false,"given":"Yan","family":"Weng","sequence":"first","affiliation":[{"name":"Computer Science, Princeton University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0801-1671","authenticated-orcid":false,"given":"Jayson","family":"Lynch","sequence":"additional","affiliation":[{"name":"EECS, MIT-CSAILL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5848-2292","authenticated-orcid":false,"given":"Elizabeth","family":"Krueger","sequence":"additional","affiliation":[{"name":"Independent researcher, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Gpt-4 technical report. arXiv preprint arXiv:2303.08774","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia\u00a0Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_2_1","first-page":"24206","article-title":"Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text","volume":"34","author":"Akbari Hassan","year":"2021","unstructured":"Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong. 2021. Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. Advances in Neural Information Processing Systems 34 (2021), 24206\u201324221.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_3_1","unstructured":"Jie Cao and Jing Xiao. 2022. An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding. In Proceedings of the 29th International Conference on Computational Linguistics Nicoletta Calzolari Chu-Ren Huang Hansaem Kim James Pustejovsky Leo Wanner Key-Sun Choi Pum-Mo Ryu Hsin-Hsi Chen Lucia Donatelli Heng Ji Sadao Kurohashi Patrizia Paggio Nianwen Xue Seokhwan Kim Younggyun Hahm Zhong He Tony\u00a0Kyungil Lee Enrico Santus Francis Bond and Seung-Hoon Na (Eds.). International Committee on Computational Linguistics Gyeongju Republic of Korea 1511\u20131520. https:\/\/aclanthology.org\/2022.coling-1.130"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.46"},{"key":"e_1_3_2_1_5_1","volume-title":"Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168","author":"Cobbe Karl","year":"2021","unstructured":"Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, 2021. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168 (2021)."},{"key":"e_1_3_2_1_6_1","unstructured":"Dan Hendrycks Collin Burns Saurav Kadavath Akul Arora Steven Basart Eric Tang Dawn Song and Jacob Steinhardt. [n. d.]. Measuring Mathematical Problem Solving With the MATH Dataset. Sort 2 4 ([n. d.]) 0\u20136."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1084"},{"key":"e_1_3_2_1_8_1","volume-title":"Figureqa: An annotated figure dataset for visual reasoning. arXiv preprint arXiv:1710.07300","author":"Kahou Samira\u00a0Ebrahimi","year":"2017","unstructured":"Samira\u00a0Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, \u00c1kos K\u00e1d\u00e1r, Adam Trischler, and Yoshua Bengio. 2017. Figureqa: An annotated figure dataset for visual reasoning. arXiv preprint arXiv:1710.07300 (2017)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.571"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1026"},{"key":"e_1_3_2_1_11_1","first-page":"3843","article-title":"Solving quantitative reasoning problems with language models","volume":"35","author":"Lewkowycz Aitor","year":"2022","unstructured":"Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, 2022. Solving quantitative reasoning problems with language models. Advances in Neural Information Processing Systems 35 (2022), 3843\u20133857.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_12_1","volume-title":"MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts. arXiv preprint arXiv:2310.02255","author":"Lu Pan","year":"2023","unstructured":"Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. 2023. MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts. arXiv preprint arXiv:2310.02255 (2023)."},{"key":"e_1_3_2_1_13_1","first-page":"2507","article-title":"Learn to explain: Multimodal reasoning via thought chains for science question answering","volume":"35","author":"Lu Pan","year":"2022","unstructured":"Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. 2022. Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems 35 (2022), 2507\u20132521.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1202"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S17-1029"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1171"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.9146"},{"key":"e_1_3_2_1_18_1","volume-title":"MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education. In NeurIPS 2021 Math AI for Education Workshop.","author":"Shen Jia\u00a0Tracy","year":"2021","unstructured":"Jia\u00a0Tracy Shen, Michiharu Yamashita, Ethan Prihar, Neil Heffernan, Xintao Wu, Ben Graff, and Dongwon Lee. 2021. MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education. In NeurIPS 2021 Math AI for Education Workshop."},{"key":"e_1_3_2_1_19_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems 27","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc\u00a0V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1088"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Zhipeng Xie and Shichao Sun. 2019. A Goal-Driven Tree-Structured Neural Model for Math Word Problems.. In Ijcai. 5299\u20135305.","DOI":"10.24963\/ijcai.2019\/736"}],"event":{"name":"ICMLT 2024: 2024 9th International Conference on Machine Learning Technologies","acronym":"ICMLT 2024","location":"Oslo Norway"},"container-title":["2024 9th International Conference on Machine Learning Technologies (ICMLT)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674029.3674041","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3674029.3674041","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T17:03:24Z","timestamp":1756487004000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674029.3674041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":21,"alternative-id":["10.1145\/3674029.3674041","10.1145\/3674029"],"URL":"https:\/\/doi.org\/10.1145\/3674029.3674041","relation":{},"subject":[],"published":{"date-parts":[[2024,5,24]]},"assertion":[{"value":"2024-09-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}