{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:57:36Z","timestamp":1782950256379,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T00:00:00Z","timestamp":1700870400000},"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":[[2023,11,27]]},"DOI":"10.1145\/3604237.3626891","type":"proceedings-article","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T18:09:47Z","timestamp":1700935787000},"page":"392-400","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-9428","authenticated-orcid":false,"given":"Lefteris","family":"Loukas","sequence":"first","affiliation":[{"name":"Helvia.ai, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5803-1138","authenticated-orcid":false,"given":"Ilias","family":"Stogiannidis","sequence":"additional","affiliation":[{"name":"Helvia.ai, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0830-4512","authenticated-orcid":false,"given":"Odysseas","family":"Diamantopoulos","sequence":"additional","affiliation":[{"name":"Helvia.ai, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0055-5598","authenticated-orcid":false,"given":"Prodromos","family":"Malakasiotis","sequence":"additional","affiliation":[{"name":"Athens University of Economics and Business, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7564-5290","authenticated-orcid":false,"given":"Stavros","family":"Vassos","sequence":"additional","affiliation":[{"name":"Helvia.ai, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_1_2_1","volume-title":"Advances in Neural Information Processing Systems, J.\u00a0Shawe-Taylor, R.\u00a0Zemel, P.\u00a0Bartlett, F.\u00a0Pereira, and K","author":"Bergstra James","year":"2011","unstructured":"James Bergstra, R\u00e9mi Bardenet, Yoshua Bengio, and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for Hyper-Parameter Optimization. In Advances in Neural Information Processing Systems, J.\u00a0Shawe-Taylor, R.\u00a0Zemel, P.\u00a0Bartlett, F.\u00a0Pereira, and K.Q. Weinberger (Eds.). Vol.\u00a024. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2011\/file\/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-5522"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3495883"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.nlp4convai-1.5"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-2029"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.607"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"e_1_3_2_1_9_1","volume-title":"Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates","author":"Christiano F","year":"2017","unstructured":"Paul\u00a0F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep Reinforcement Learning from Human Preferences. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf"},{"key":"e_1_3_2_1_10_1","volume-title":"Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces. ArXiv abs\/1805.10190","author":"Coucke Alice","year":"2018","unstructured":"Alice Coucke, Alaa Saade, Adrien Ball, Th\u00e9odore Bluche, Alexandre Caulier, David Leroy, Cl\u00e9ment Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Ma\u00ebl Primet, and Joseph Dureau. 2018. Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces. ArXiv abs\/1805.10190 (2018)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_1_12_1","unstructured":"Jesse Dodge Gabriel Ilharco Roy Schwartz Ali Farhadi Hannaneh Hajishirzi and Noah Smith. 2020. Fine-Tuning Pretrained Language Models: Weight Initializations Data Orders and Early Stopping. arxiv:2002.06305\u00a0[cs.CL]"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Abu Dhabi, UAE, 298\u2013310","author":"Gabrielle","year":"2022","unstructured":"Gabrielle Gauthier-melancon, Orlando Marquez\u00a0Ayala, Lindsay Brin, Chris Tyler, Frederic Branchaud-charron, Joseph Marinier, Karine Grande, and Di Le. 2022. Azimuth: Systematic Error Analysis for Text Classification. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Abu Dhabi, UAE, 298\u2013310. https:\/\/aclanthology.org\/2022.emnlp-demos.30"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMC249363.2019"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1131"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3496517"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.finnlp-1.8"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"key":"e_1_3_2_1_22_1","unstructured":"Nelson\u00a0F. Liu Kevin Lin John Hewitt Ashwin Paranjape Michele Bevilacqua Fabio Petroni and Percy Liang. 2023. Lost in the Middle: How Language Models Use Long Contexts. arxiv:2307.03172\u00a0[cs.CL]"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-9323-9_15"},{"key":"e_1_3_2_1_24_1","volume-title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach. ArXiv abs\/1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. ArXiv abs\/1907.11692 (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.econlp-1.2"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.303"},{"key":"e_1_3_2_1_27_1","volume-title":"Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting. -, Macao, 74\u201380","author":"Loukas Lefteris","year":"2023","unstructured":"Lefteris Loukas, Ilias Stogiannidis, Prodromos Malakasiotis, and Stavros Vassos. 2023. Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance. In Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting. -, Macao, 74\u201380. https:\/\/aclanthology.org\/2023.finnlp-1.7"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.237"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"e_1_3_2_1_30_1","volume-title":"Advances in Neural Information Processing Systems, S.\u00a0Koyejo, S.\u00a0Mohamed, A.\u00a0Agarwal, D.\u00a0Belgrave, K.\u00a0Cho, and A.\u00a0Oh (Eds.). Vol.\u00a035. Curran Associates","author":"AI.","year":"2022","unstructured":"OpenAI. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, S.\u00a0Koyejo, S.\u00a0Mohamed, A.\u00a0Agarwal, D.\u00a0Belgrave, K.\u00a0Cho, and A.\u00a0Oh (Eds.). Vol.\u00a035. Curran Associates, Inc., 27730\u201327744. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/b1efde53be364a73914f58805a001731-Paper-Conference.pdf"},{"key":"e_1_3_2_1_31_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. arxiv:2303.08774\u00a0[cs.CL]"},{"key":"e_1_3_2_1_32_1","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35 (2022), 27730\u201327744.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_33_1","volume-title":"Improving Language Understanding with Unsupervised Learning. https:\/\/openai.com\/research\/language-unsupervised Accessed","author":"Radford Alec","year":"2023","unstructured":"Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving Language Understanding with Unsupervised Learning. https:\/\/openai.com\/research\/language-unsupervised Accessed: 06 May 2023."},{"key":"e_1_3_2_1_34_1","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"key":"e_1_3_2_1_35_1","first-page":"59","volume-title":"Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2022","author":"Razeghi Yasaman","year":"2022","unstructured":"Yasaman Razeghi, Robert\u00a0L Logan\u00a0IV, Matt Gardner, and Sameer Singh. 2022. Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 840\u2013854. https:\/\/aclanthology.org\/2022.findings-emnlp.59"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411763.3451760"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.nlp4convai-1.5"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Song Kaitao","year":"2020","unstructured":"Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020. MPNet: Masked and Permuted Pre-Training for Language Understanding. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS\u201920). Curran Associates Inc., Red Hook, NY, USA, Article 1414, 11\u00a0pages."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.1000"},{"key":"e_1_3_2_1_40_1","unstructured":"Hugo Touvron and Meta GenAI. 2023. Llama 2: Open Foundation and Fine-Tuned Chat Models. arxiv:2307.09288\u00a0[cs.CL]"},{"key":"e_1_3_2_1_41_1","volume-title":"Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg.","author":"Tunstall Lewis","year":"2022","unstructured":"Lewis Tunstall, Nils Reimers, Unso Eun\u00a0Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg. 2022. Efficient Few-Shot Learning Without Prompts. ArXiv abs\/2209.11055 (2022)."},{"key":"e_1_3_2_1_42_1","unstructured":"Ashish Vaswani Noam\u00a0M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention Is All you Need. Advances in neural information processing systems 5998\u20136008."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.167"},{"key":"e_1_3_2_1_44_1","volume-title":"Chi, Quoc Le, and Denny Zhou","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_45_1","volume-title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding","author":"Yang Zhilin","unstructured":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc\u00a0V. Le. 2019. XLNet: Generalized Autoregressive Pretraining for Language Understanding. Curran Associates Inc., Red Hook, NY, USA."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.insights-1.19"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.192"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490354.3494453"}],"event":{"name":"ICAIF '23: 4th ACM International Conference on AI in Finance","location":"Brooklyn NY USA","acronym":"ICAIF '23"},"container-title":["4th ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604237.3626891","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3604237.3626891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:35:53Z","timestamp":1755884153000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604237.3626891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,25]]},"references-count":48,"alternative-id":["10.1145\/3604237.3626891","10.1145\/3604237"],"URL":"https:\/\/doi.org\/10.1145\/3604237.3626891","relation":{},"subject":[],"published":{"date-parts":[[2023,11,25]]},"assertion":[{"value":"2023-11-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}