{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T16:12:01Z","timestamp":1758125521895,"version":"3.33.0"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,15]]},"DOI":"10.1109\/bigdata62323.2024.10825021","type":"proceedings-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:31:23Z","timestamp":1737052283000},"page":"1919-1928","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Financial Reasoning in Large Language Models: The Role of Gold Facts"],"prefix":"10.1109","author":[{"given":"Shoutai","family":"Zhu","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology,China"}]},{"given":"Ziqiang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology,China"}]},{"given":"Kaiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Central University of Finance and Economics,China"}]},{"given":"Yishu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology,China"}]},{"given":"Wenqi","family":"Wei","sequence":"additional","affiliation":[{"name":"Fordham University,USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.300"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i11.26543"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.454"},{"article-title":"Dataset and neural recurrent sequence labeling model for open-domain factoid question answering","year":"2016","author":"Li","key":"ref4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883080"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.248"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1133"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271683"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.564"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3677052.3698685"},{"key":"ref11","first-page":"27 730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang","year":"2022","journal-title":"Advances in neural information processing systems"},{"article-title":"Finetuned language models are zero-shot learners","volume-title":"International Conference on Learning Representations","author":"Wei","key":"ref12"},{"article-title":"Least-to-most prompting enables complex reasoning in large language models","volume-title":"The Eleventh International Conference on Learning Representations","author":"Zhou","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2024.3524083"},{"key":"ref15","article-title":"Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks","author":"Chen","year":"2023","journal-title":"Transactions on Machine Learning Research"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422554"},{"key":"ref17","first-page":"13 263","article-title":"The privacy onion effect: Memorization is relative","volume-title":"Conference on Neural Information Processing Systems","volume":"35","author":"Carlini"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134077"},{"article-title":"Machine learning for synthetic data generation: A review","year":"2023","author":"Lu","key":"ref19"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3534642"},{"article-title":"Tabfact: A large-scale dataset for table-based fact verification","volume-title":"International Conference on Learning Representations","author":"Chen","key":"ref21"},{"key":"ref22","first-page":"2368","article-title":"Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs","volume-title":"the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Dua"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.91"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-4007"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.254"},{"article-title":"Multi-view graph representation learning for answering hybrid numerical reasoning question","year":"2023","author":"Wei","key":"ref26"},{"key":"ref27","first-page":"1379","article-title":"Answering numerical reasoning questions in table-text hybrid contents with graph-based encoder and tree-based decoder","volume-title":"International Conference on Computational Linguistics","author":"Lei"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231757"},{"key":"ref29","first-page":"97","article-title":"Ctab-gan: Effective table data synthesizing","volume-title":"Asian Conference on Machine Learning","author":"Zhao"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/PRDC47002.2019.00050"},{"key":"ref31","article-title":"Modeling tabular data using conditional gan","volume":"32","author":"Xu","year":"2019","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2024 IEEE International Conference on Big Data (BigData)","start":{"date-parts":[[2024,12,15]]},"location":"Washington, DC, USA","end":{"date-parts":[[2024,12,18]]}},"container-title":["2024 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10824975\/10824942\/10825021.pdf?arnumber=10825021","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T07:45:03Z","timestamp":1737099903000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10825021\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/bigdata62323.2024.10825021","relation":{},"subject":[],"published":{"date-parts":[[2024,12,15]]}}}