{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:06:37Z","timestamp":1772906797909,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,14]]},"DOI":"10.1145\/3677052.3698682","type":"proceedings-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T06:38:06Z","timestamp":1731566286000},"page":"266-273","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipeline"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1952-931X","authenticated-orcid":false,"given":"Kuldeep","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Statistics, Michigan State University, US"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5863-4749","authenticated-orcid":false,"given":"Simerjot","family":"Kaur","sequence":"additional","affiliation":[{"name":"JP Morgan Chase, US"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5575-2313","authenticated-orcid":false,"given":"Charese","family":"Smiley","sequence":"additional","affiliation":[{"name":"JP Morgan Chase, US"}]}],"member":"320","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Rohan Anil Andrew\u00a0M. Dai Orhan Firat Melvin Johnson Dmitry Lepikhin Alexandre Passos Siamak Shakeri Emanuel Taropa Paige Bailey Zhifeng Chen Eric Chu Jonathan\u00a0H. Clark Laurent\u00a0El Shafey Yanping Huang Kathy Meier-Hellstern Gaurav Mishra Erica Moreira Mark Omernick Kevin Robinson Sebastian Ruder Yi Tay Kefan Xiao Yuanzhong Xu Yujing Zhang Gustavo\u00a0Hernandez Abrego Junwhan Ahn Jacob Austin Paul Barham Jan Botha James Bradbury Siddhartha Brahma Kevin Brooks Michele Catasta Yong Cheng Colin Cherry Christopher\u00a0A. Choquette-Choo Aakanksha Chowdhery Cl\u00e9ment Crepy Shachi Dave Mostafa Dehghani Sunipa Dev Jacob Devlin Mark D\u00edaz Nan Du Ethan Dyer Vlad Feinberg Fangxiaoyu Feng Vlad Fienber Markus Freitag Xavier Garcia Sebastian Gehrmann Lucas Gonzalez Guy Gur-Ari Steven Hand Hadi Hashemi Le Hou Joshua Howland Andrea Hu Jeffrey Hui Jeremy Hurwitz Michael Isard Abe Ittycheriah Matthew Jagielski Wenhao Jia Kathleen Kenealy Maxim Krikun Sneha Kudugunta Chang Lan Katherine Lee Benjamin Lee Eric Li Music Li Wei Li YaGuang Li Jian Li Hyeontaek Lim Hanzhao Lin Zhongtao Liu Frederick Liu Marcello Maggioni Aroma Mahendru Joshua Maynez Vedant Misra Maysam Moussalem Zachary Nado John Nham Eric Ni Andrew Nystrom Alicia Parrish Marie Pellat Martin Polacek Alex Polozov Reiner Pope Siyuan Qiao Emily Reif Bryan Richter Parker Riley Alex\u00a0Castro Ros Aurko Roy Brennan Saeta Rajkumar Samuel Renee Shelby Ambrose Slone Daniel Smilkov David\u00a0R. So Daniel Sohn Simon Tokumine Dasha Valter Vijay Vasudevan Kiran Vodrahalli Xuezhi Wang Pidong Wang Zirui Wang Tao Wang John Wieting Yuhuai Wu Kelvin Xu Yunhan Xu Linting Xue Pengcheng Yin Jiahui Yu Qiao Zhang Steven Zheng Ce Zheng Weikang Zhou Denny Zhou Slav Petrov and Yonghui Wu. 2023. PaLM 2 Technical Report. arxiv:2305.10403\u00a0[cs.CL]"},{"key":"e_1_3_2_1_2_1","volume-title":"The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=hSyW5go0v8","author":"Asai Akari","year":"2024","unstructured":"Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=hSyW5go0v8"},{"key":"e_1_3_2_1_3_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates","author":"Brown Tom","year":"1877","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared\u00a0D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc., 1877\u20131901. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"e_1_3_2_1_4_1","unstructured":"Tom\u00a0B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel\u00a0M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. arxiv:2005.14165\u00a0[cs.CL]"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1171"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Wenhu Chen. 2023. Large Language Models are few(1)-shot Table Reasoners. arxiv:2210.06710\u00a0[cs.CL]","DOI":"10.18653\/v1\/2023.findings-eacl.83"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.300"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.99"},{"key":"e_1_3_2_1_9_1","unstructured":"Yunfan Gao Yun Xiong Xinyu Gao Kangxiang Jia Jinliu Pan Yuxi Bi Yi Dai Jiawei Sun Meng Wang and Haofen Wang. 2024. Retrieval-Augmented Generation for Large Language Models: A Survey. arxiv:2312.10997\u00a0[cs.CL]"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning(ICML\u201920)","author":"Guu Kelvin","year":"2020","unstructured":"Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020. REALM: Retrieval-Augmented Language Model Pre-Training. In Proceedings of the 37th International Conference on Machine Learning(ICML\u201920). JMLR.org, Article 368, 10\u00a0pages."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1612"},{"key":"e_1_3_2_1_12_1","unstructured":"Xianzhi Li Samuel Chan Xiaodan Zhu Yulong Pei Zhiqiang Ma Xiaomo Liu and Sameena Shah. 2023. Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks. arxiv:2305.05862\u00a0[cs.CL]"},{"key":"e_1_3_2_1_13_1","unstructured":"Xiao Li Yin Zhu Sichen Liu Jiangzhou Ju Yuzhong Qu and Gong Cheng. 2022. DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data. arxiv:2211.12668\u00a0[cs.CL]"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1260"},{"key":"e_1_3_2_1_15_1","volume-title":"Tyna\u00a0Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe\u00a0Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng.","author":"Neelakantan Arvind","year":"2022","unstructured":"Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse\u00a0Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong\u00a0Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna\u00a0Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe\u00a0Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng. 2022. Text and Code Embeddings by Contrastive Pre-Training. arxiv:2201.10005\u00a0[cs.CL]"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271702"},{"key":"e_1_3_2_1_17_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. arxiv:2303.08774\u00a0[cs.CL]"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.302"},{"key":"e_1_3_2_1_19_1","volume-title":"Visconde: Multi-document QA with GPT-3 and Neural Reranking. arxiv:2212.09656\u00a0[cs.CL]","author":"Pereira Jayr","year":"2022","unstructured":"Jayr Pereira, Robson Fidalgo, Roberto Lotufo, and Rodrigo Nogueira. 2022. Visconde: Multi-document QA with GPT-3 and Neural Reranking. arxiv:2212.09656\u00a0[cs.CL]"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Ofir Press Muru Zhang Sewon Min Ludwig Schmidt Noah\u00a0A. Smith and Mike Lewis. 2023. Measuring and Narrowing the Compositionality Gap in Language Models. arxiv:2210.03350\u00a0[cs.CL]","DOI":"10.18653\/v1\/2023.findings-emnlp.378"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5121\/ijnlc.2024.13103"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arxiv:1908.10084\u00a0[cs.CL]","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1561\/1500000019"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/0306-4573(88)90021-0"},{"key":"e_1_3_2_1_25_1","volume-title":"APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning. arxiv:2212.07249\u00a0[cs.CL]","author":"Sun Jiashuo","year":"2023","unstructured":"Jiashuo Sun, Hang Zhang, Chen Lin, Yeyun Gong, Jian Guo, and Nan Duan. 2023. APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning. arxiv:2212.07249\u00a0[cs.CL]"},{"key":"e_1_3_2_1_26_1","unstructured":"Yixuan Tang and Yi Yang. 2024. MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries. arxiv:2401.15391\u00a0[cs.CL]"},{"key":"e_1_3_2_1_27_1","unstructured":"Jinqiang Wang Tao Zhu Liming Chen Huansheng Ning and Yaping Wan. 2022. Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition. arxiv:2203.12230\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2203.12230"},{"key":"e_1_3_2_1_28_1","volume-title":"AAAI Conference on Artificial Intelligence. https:\/\/api.semanticscholar.org\/CorpusID:19178620","author":"Wang Shuohang","year":"2018","unstructured":"Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, and Jing Jiang. 2018. R3: Reinforced Ranker-Reader for Open-Domain Question Answering. In AAAI Conference on Artificial Intelligence. https:\/\/api.semanticscholar.org\/CorpusID:19178620"},{"key":"e_1_3_2_1_29_1","volume-title":"Aakanksha Chowdhery, and Denny Zhou.","author":"Wang Xuezhi","year":"2023","unstructured":"Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-Consistency Improves Chain of Thought Reasoning in Language Models. arxiv:2203.11171\u00a0[cs.CL]"},{"key":"e_1_3_2_1_30_1","volume-title":"Chi, Quoc Le, and Denny Zhou","author":"Wei Jason","year":"2023","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arxiv:2201.11903\u00a0[cs.CL]"},{"key":"e_1_3_2_1_31_1","unstructured":"Shunyu Yao Jeffrey Zhao Dian Yu Nan Du Izhak Shafran Karthik Narasimhan and Yuan Cao. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. arxiv:2210.03629\u00a0[cs.CL]"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Xinyi Zheng Doug Burdick Lucian Popa Xu Zhong and Nancy Xin\u00a0Ru Wang. 2020. Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context. arxiv:2005.00589\u00a0[cs.CV]","DOI":"10.1109\/WACV48630.2021.00074"}],"event":{"name":"ICAIF '24: 5th ACM International Conference on AI in Finance","location":"Brooklyn NY USA","acronym":"ICAIF '24"},"container-title":["Proceedings of the 5th ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677052.3698682","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3677052.3698682","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:09:26Z","timestamp":1755882566000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677052.3698682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":32,"alternative-id":["10.1145\/3677052.3698682","10.1145\/3677052"],"URL":"https:\/\/doi.org\/10.1145\/3677052.3698682","relation":{},"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"2024-11-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}