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However, general-domain LLMs often struggle with financial tasks due to the technical and specialized nature of financial texts. This study investigates the efficacy of instruction fine-tuning smaller-scale LLMs, including Mistral-7B, Llama3-8B, and Phi3-mini, to enhance their performance in financial text classification tasks. We fine-tuned both instruction-tuned and base models across four financial classification tasks, achieving significant improvements in task-specific performance. Furthermore, we evaluated the zero-shot capabilities of these fine-tuned models on three unseen complex financial tasks, including argument classification, deal completeness classification, and causal classification. Our results indicate while base model fine-tuning led to greater degradation, instruction-tuned models maintained more robust performance. To address this degradation, we employed model merging techniques, integrating single-task domain-specific fine-tuned models with the base model. Using this merging method resulted in significant enhancements in zero-shot performance, even exceeding the original model\u2019s accuracy on certain datasets. Our findings underscore the effectiveness of instruction fine-tuning and model merging for adapting LLMs to specialized financial text classification tasks.<\/jats:p>","DOI":"10.1145\/3706119","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:57:46Z","timestamp":1733309866000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["A Comparative Analysis of Instruction Fine-Tuning Large Language Models for Financial Text Classification"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5163-2541","authenticated-orcid":false,"given":"Sorouralsadat","family":"Fatemi","sequence":"first","affiliation":[{"name":"University of Illinois Chicago, Chicago, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-1238","authenticated-orcid":false,"given":"Yuheng","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago, Chicago, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5634-1363","authenticated-orcid":false,"given":"Maryam","family":"Mousavi","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"Phi-3 technical report: A highly capable language model locally on your phone","author":"Abdin Marah","year":"2024","unstructured":"Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et\u00a0al. 2024. Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219 (2024).","journal-title":"arXiv preprint arXiv:2404.14219"},{"key":"e_1_3_3_3_2","article-title":"Gpt-4 technical report","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et\u00a0al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).","journal-title":"arXiv preprint arXiv:2303.08774"},{"key":"e_1_3_3_4_2","article-title":"Large language models for conducting advanced text Analytics Information Systems Research","author":"Ampel Benjamin","year":"2024","unstructured":"Benjamin Ampel, Chi-Heng Yang, James Hu, and Hsinchun Chen. 2024. Large language models for conducting advanced text Analytics Information Systems Research. ACM Transactions on Management Information Systems (2024).","journal-title":"ACM Transactions on Management Information Systems"},{"key":"e_1_3_3_5_2","article-title":"In-context learning with long-context models: An in-depth exploration","author":"Bertsch Amanda","year":"2024","unstructured":"Amanda Bertsch, Maor Ivgi, Uri Alon, Jonathan Berant, Matthew R. Gormley, and Graham Neubig. 2024. In-context learning with long-context models: An in-depth exploration. arXiv preprint arXiv:2405.00200 (2024).","journal-title":"arXiv preprint arXiv:2405.00200"},{"key":"e_1_3_3_6_2","unstructured":"T. B. Brown B. Mann N. Ryder M. Subbiah J. D. Kaplan P. Dhariwal A. Neelakantan P. Shyam G. Sastry A. Askell et\u00a0al. 2020. Language models are few-shot learners advances in neural information processing systems 33. (2020)."},{"key":"e_1_3_3_7_2","unstructured":"OpenAI ChatGPT. 2023. optimizing Language Models for Dialogue. OpenAI. 2022. (2023)."},{"key":"e_1_3_3_8_2","article-title":"Distinguish before answer: Generating contrastive explanation as knowledge for commonsense question answering","author":"Chen Qianglong","year":"2023","unstructured":"Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang, Fei Huang, Luo Si, and Yin Zhang. 2023. Distinguish before answer: Generating contrastive explanation as knowledge for commonsense question answering. arXiv preprint arXiv:2305.08135 (2023).","journal-title":"arXiv preprint arXiv:2305.08135"},{"key":"e_1_3_3_9_2","article-title":"Adapting large language models via reading comprehension","author":"Cheng Daixuan","year":"2023","unstructured":"Daixuan Cheng, Shaohan Huang, and Furu Wei. 2023. Adapting large language models via reading comprehension. arXiv preprint arXiv:2309.09530 (2023).","journal-title":"arXiv preprint arXiv:2309.09530"},{"issue":"3","key":"e_1_3_3_10_2","first-page":"6","article-title":"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality","volume":"2","author":"Chiang Wei-Lin","year":"2023","unstructured":"Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, et\u00a0al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https:\/\/vicuna. lmsys. org (accessed 14 April 2023) 2, 3 (2023), 6.","journal-title":"See https:\/\/vicuna. lmsys. org (accessed 14 April 2023)"},{"issue":"240","key":"e_1_3_3_11_2","first-page":"1","article-title":"Palm: Scaling language modeling with pathways","volume":"24","author":"Chowdhery Aakanksha","year":"2023","unstructured":"Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et\u00a0al. 2023. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24, 240 (2023), 1\u2013113.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543873.3587324"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580480"},{"key":"e_1_3_3_14_2","article-title":"Structured information extraction from complex scientific text with fine-tuned large language models","author":"Dunn Alexander","year":"2022","unstructured":"Alexander Dunn, John Dagdelen, Nicholas Walker, Sanghoon Lee, Andrew S. Rosen, Gerbrand Ceder, Kristin Persson, and Anubhav Jain. 2022. Structured information extraction from complex scientific text with fine-tuned large language models. arXiv preprint arXiv:2212.05238 (2022).","journal-title":"arXiv preprint arXiv:2212.05238"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1993.tb04750.x"},{"key":"e_1_3_3_16_2","article-title":"A comparative analysis of fine-tuned LLMs and few-shot learning of LLMs for financial sentiment analysis","author":"Fatemi Sorouralsadat","year":"2023","unstructured":"Sorouralsadat Fatemi and Yuheng Hu. 2023. A comparative analysis of fine-tuned LLMs and few-shot learning of LLMs for financial sentiment analysis. arXiv preprint arXiv:2312.08725 (2023).","journal-title":"arXiv preprint arXiv:2312.08725"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2023.100508"},{"key":"e_1_3_3_18_2","article-title":"Arcee\u2019s MergeKit: A toolkit for merging large language models","author":"Goddard Charles","year":"2024","unstructured":"Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vlad Karpukhin, Brian Benedict, Mark McQuade, and Jacob Solawetz. 2024. Arcee\u2019s MergeKit: A toolkit for merging large language models. arXiv preprint arXiv:2403.13257 (2024).","journal-title":"arXiv preprint arXiv:2403.13257"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jempfin.2010.11.009"},{"key":"e_1_3_3_20_2","article-title":"LLaMA3 Model","author":"Group Meta","year":"2024","unstructured":"Meta Group. 2024. LLaMA3 Model. https:\/\/ai.meta.com\/blog\/meta-llama-3\/. (2024). Accessed: 2024-04-18.","journal-title":"https:\/\/ai.meta.com\/blog\/meta-llama-3\/"},{"key":"e_1_3_3_21_2","article-title":"Lora: Low-rank adaptation of large language models","author":"Hu Edward J.","year":"2021","unstructured":"Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021).","journal-title":"arXiv preprint arXiv:2106.09685"},{"key":"e_1_3_3_22_2","article-title":"Lawyer llama technical report","author":"Huang Quzhe","year":"2023","unstructured":"Quzhe Huang, Mingxu Tao, Chen Zhang, Zhenwei An, Cong Jiang, Zhibin Chen, Zirui Wu, and Yansong Feng. 2023. Lawyer llama technical report. arXiv preprint arXiv:2305.15062 (2023).","journal-title":"arXiv preprint arXiv:2305.15062"},{"key":"e_1_3_3_23_2","article-title":"Editing models with task arithmetic","author":"Ilharco Gabriel","year":"2022","unstructured":"Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2022. Editing models with task arithmetic. arXiv preprint arXiv:2212.04089 (2022).","journal-title":"arXiv preprint arXiv:2212.04089"},{"key":"e_1_3_3_24_2","article-title":"Financebench: A new benchmark for financial question answering","author":"Islam Pranab","year":"2023","unstructured":"Pranab Islam, Anand Kannappan, Douwe Kiela, Rebecca Qian, Nino Scherrer, and Bertie Vidgen. 2023. Financebench: A new benchmark for financial question answering. arXiv preprint arXiv:2311.11944 (2023).","journal-title":"arXiv preprint arXiv:2311.11944"},{"key":"e_1_3_3_25_2","article-title":"Mistral 7B","author":"Jiang Albert Q.","year":"2023","unstructured":"Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et\u00a0al. 2023. Mistral 7B. arXiv preprint arXiv:2310.06825 (2023).","journal-title":"arXiv preprint arXiv:2310.06825"},{"key":"e_1_3_3_26_2","article-title":"From beginner to expert: Modeling medical knowledge into general LLMs","author":"Li Qiang","year":"2023","unstructured":"Qiang Li, Xiaoyan Yang, Haowen Wang, Qin Wang, Lei Liu, Junjie Wang, Yang Zhang, Mingyuan Chu, Sen Hu, Yicheng Chen, et\u00a0al. 2023. From beginner to expert: Modeling medical knowledge into general LLMs. arXiv preprint arXiv:2312.01040 (2023).","journal-title":"arXiv preprint arXiv:2312.01040"},{"key":"e_1_3_3_27_2","first-page":"408","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track","author":"Li Xianzhi","year":"2023","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. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track. 408\u2013422."},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604237.3626869"},{"key":"e_1_3_3_29_2","article-title":"Tuning language models by proxy","author":"Liu Alisa","year":"2024","unstructured":"Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, and Noah A. Smith. 2024. Tuning language models by proxy. arXiv preprint arXiv:2401.08565 (2024).","journal-title":"arXiv preprint arXiv:2401.08565"},{"key":"e_1_3_3_30_2","article-title":"GOAT: Fine-tuned Llama outperforms GPT-4 on arithmetic tasks","author":"Liu Tiedong","year":"2023","unstructured":"Tiedong Liu and Bryan Kian Hsiang Low. 2023. GOAT: Fine-tuned Llama outperforms GPT-4 on arithmetic tasks. arXiv preprint arXiv:2305.14201 (2023).","journal-title":"arXiv preprint arXiv:2305.14201"},{"key":"e_1_3_3_31_2","article-title":"Can ChatGPT forecast stock price movements? Return predictability and large language models","author":"Lopez-Lira Alejandro","year":"2023","unstructured":"Alejandro Lopez-Lira and Yuehua Tang. 2023. Can ChatGPT forecast stock price movements? Return predictability and large language models. arXiv preprint arXiv:2304.07619 (2023).","journal-title":"arXiv preprint arXiv:2304.07619"},{"key":"e_1_3_3_32_2","volume-title":"Rethinking Finance: Perspectives on the Crisis (Proceedings of a conference). Russel Sage Foundation","author":"Malkiel Burton G.","year":"2011","unstructured":"Burton G. Malkiel. 2011. The efficient-market hypothesis and the financial crisis. In Rethinking Finance: Perspectives on the Crisis (Proceedings of a conference). Russel Sage Foundation. Citeseer."},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23062"},{"key":"e_1_3_3_34_2","article-title":"Financial document causality detection shared task (fincausal 2020)","author":"Mariko Dominique","year":"2020","unstructured":"Dominique Mariko, Hanna Abi Akl, Estelle Labidurie, Stephane Durfort, Hugues De Mazancourt, and Mahmoud El-Haj. 2020. Financial document causality detection shared task (fincausal 2020). arXiv preprint arXiv:2012.02505 (2020).","journal-title":"arXiv preprint arXiv:2012.02505"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3009626"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512016"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3508230.3508247"},{"key":"e_1_3_3_38_2","article-title":"Pillars of grammatical error correction: Comprehensive inspection of contemporary approaches in the Era of large language models","author":"Omelianchuk Kostiantyn","year":"2024","unstructured":"Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi, Artem Chernodub, Oleksandr Korniienko, and Igor Samokhin. 2024. Pillars of grammatical error correction: Comprehensive inspection of contemporary approaches in the Era of large language models. arXiv preprint arXiv:2404.14914 (2024).","journal-title":"arXiv preprint arXiv:2404.14914"},{"issue":"5","key":"e_1_3_3_39_2","article-title":"Gpt-4 technical report. arxiv 2303.08774","volume":"2","author":"OpenAI R.","year":"2023","unstructured":"R. OpenAI. 2023. Gpt-4 technical report. arxiv 2303.08774. View in Article 2, 5 (2023).","journal-title":"View in Article"},{"key":"e_1_3_3_40_2","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, et\u00a0al. 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_3_41_2","article-title":"Trillion dollar words: A new financial dataset, task & market analysis","author":"Shah Agam","year":"2023","unstructured":"Agam Shah, Suvan Paturi, and Sudheer Chava. 2023. Trillion dollar words: A new financial dataset, task & market analysis. arXiv preprint arXiv:2305.07972 (2023).","journal-title":"arXiv preprint arXiv:2305.07972"},{"key":"e_1_3_3_42_2","article-title":"When flue meets flang: Benchmarks and large pre-trained language model for financial domain","author":"Shah Raj Sanjay","year":"2022","unstructured":"Raj Sanjay Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah, Wendi Du, Sudheer Chava, Natraj Raman, Charese Smiley, Jiaao Chen, and Diyi Yang. 2022. When flue meets flang: Benchmarks and large pre-trained language model for financial domain. arXiv preprint arXiv:2211.00083 (2022).","journal-title":"arXiv preprint arXiv:2211.00083"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487553.3524637"},{"key":"e_1_3_3_44_2","article-title":"Towards expert-level medical question answering with large language models","author":"Singhal Karan","year":"2023","unstructured":"Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, et\u00a0al. 2023. Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617 (2023).","journal-title":"arXiv preprint arXiv:2305.09617"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-73103-8_41"},{"key":"e_1_3_3_46_2","article-title":"Text classification via large language models","author":"Sun Xiaofei","year":"2023","unstructured":"Xiaofei Sun, Xiaoya Li, Jiwei Li, Fei Wu, Shangwei Guo, Tianwei Zhang, and Guoyin Wang. 2023. Text classification via large language models. arXiv preprint arXiv:2305.08377 (2023).","journal-title":"arXiv preprint arXiv:2305.08377"},{"key":"e_1_3_3_47_2","first-page":"242","volume-title":"Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING\u201923)","author":"Sy Eugene","year":"2023","unstructured":"Eugene Sy, Tzu-Cheng Peng, Shih-Hsuan Huang, Heng-Yu Lin, and Yung-Chun Chang. 2023. Fine-grained argument understanding with bert ensemble techniques: A deep dive into financial sentiment analysis. In Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING\u201923). 242\u2013249."},{"key":"e_1_3_3_48_2","unstructured":"Rohan Taori Ishaan Gulrajani Tianyi Zhang Yann Dubois Xuechen Li Carlos Guestrin Percy Liang and Tatsunori B. Hashimoto. 2023. Stanford Alpaca: An Instruction-following Llama Model. (2023)."},{"key":"e_1_3_3_49_2","article-title":"Huatuo: Tuning Llama model with Chinese medical knowledge","author":"Wang Haochun","year":"2023","unstructured":"Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, and Ting Liu. 2023. Huatuo: Tuning Llama model with Chinese medical knowledge. arXiv preprint arXiv:2304.06975 (2023).","journal-title":"arXiv preprint arXiv:2304.06975"},{"key":"e_1_3_3_50_2","article-title":"FingPT: Instruction tuning benchmark for open-source large language models in financial datasets","author":"Wang Neng","year":"2023","unstructured":"Neng Wang, Hongyang Yang, and Christina Dan Wang. 2023. FingPT: Instruction tuning benchmark for open-source large language models in financial datasets. arXiv preprint arXiv:2310.04793 (2023).","journal-title":"arXiv preprint arXiv:2310.04793"},{"key":"e_1_3_3_51_2","article-title":"InstructUIE: Multi-task instruction tuning for unified information extraction","author":"Wang Xiao","year":"2023","unstructured":"Xiao Wang, Weikang Zhou, Can Zu, Han Xia, Tianze Chen, Yuansen Zhang, Rui Zheng, Junjie Ye, Qi Zhang, Tao Gui, et\u00a0al. 2023. InstructUIE: Multi-task instruction tuning for unified information extraction. arXiv preprint arXiv:2304.08085 (2023).","journal-title":"arXiv preprint arXiv:2304.08085"},{"key":"e_1_3_3_52_2","article-title":"Is ChatGPT a good sentiment analyzer? A preliminary study","author":"Wang Zengzhi","year":"2023","unstructured":"Zengzhi Wang, Qiming Xie, Yi Feng, Zixiang Ding, Zinong Yang, and Rui Xia. 2023. Is ChatGPT a good sentiment analyzer? A preliminary study. arXiv preprint arXiv:2304.04339 (2023).","journal-title":"arXiv preprint arXiv:2304.04339"},{"key":"e_1_3_3_53_2","article-title":"Larger language models do in-context learning differently","author":"Wei Jerry","year":"2023","unstructured":"Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et\u00a0al. 2023. Larger language models do in-context learning differently. arXiv preprint arXiv:2303.03846 (2023).","journal-title":"arXiv preprint arXiv:2303.03846"},{"key":"e_1_3_3_54_2","article-title":"BloombergGPT: A large language model for finance","author":"Wu Shijie","year":"2023","unstructured":"Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023. BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564 (2023).","journal-title":"arXiv preprint arXiv:2303.17564"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102943"},{"key":"e_1_3_3_56_2","article-title":"The FinBen: An holistic financial benchmark for large language models","author":"Xie Qianqian","year":"2024","unstructured":"Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, et\u00a0al. 2024. The FinBen: An holistic financial benchmark for large language models. arXiv preprint arXiv:2402.12659 (2024).","journal-title":"arXiv preprint arXiv:2402.12659"},{"key":"e_1_3_3_57_2","article-title":"The Wall Street neophyte: A zero-shot analysis of ChatGPT over multimodal stock movement prediction challenges","author":"Xie Qianqian","year":"2023","unstructured":"Qianqian Xie, Weiguang Han, Yanzhao Lai, Min Peng, and Jimin Huang. 2023. The Wall Street neophyte: A zero-shot analysis of ChatGPT over multimodal stock movement prediction challenges. arXiv preprint arXiv:2304.05351 (2023).","journal-title":"arXiv preprint arXiv:2304.05351"},{"key":"e_1_3_3_58_2","article-title":"PIXIU: A comprehensive benchmark, instruction dataset and large language model for finance","volume":"36","author":"Xie Qianqian","year":"2024","unstructured":"Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, and Jimin Huang. 2024. PIXIU: A comprehensive benchmark, instruction dataset and large language model for finance. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3688399"},{"key":"e_1_3_3_60_2","article-title":"Resolving interference when merging models","volume":"2","author":"Yadav Prateek","year":"2023","unstructured":"Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal. 2023. Resolving interference when merging models. arXiv preprint arXiv:2306.01708 2 (2023).","journal-title":"arXiv preprint arXiv:2306.01708"},{"key":"e_1_3_3_61_2","article-title":"FinGPT: Open-source financial large language models","author":"Yang Hongyang","year":"2023","unstructured":"Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023. FinGPT: Open-source financial large language models. arXiv preprint arXiv:2306.06031 (2023).","journal-title":"arXiv preprint arXiv:2306.06031"},{"key":"e_1_3_3_62_2","article-title":"Generating plausible counterfactual explanations for deep transformers in financial text classification","author":"Yang Linyi","year":"2020","unstructured":"Linyi Yang, Eoin M. Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, and Ruihai Dong. 2020. Generating plausible counterfactual explanations for deep transformers in financial text classification. arXiv preprint arXiv:2010.12512 (2020).","journal-title":"arXiv preprint arXiv:2010.12512"},{"key":"e_1_3_3_63_2","article-title":"Financial aspect-based sentiment analysis using deep representations","author":"Yang Steve","year":"2018","unstructured":"Steve Yang, Jason Rosenfeld, and Jacques Makutonin. 2018. Financial aspect-based sentiment analysis using deep representations. arXiv preprint arXiv:1808.07931 (2018).","journal-title":"arXiv preprint arXiv:1808.07931"},{"key":"e_1_3_3_64_2","article-title":"InvestLM: A large language model for investment using financial domain instruction tuning","author":"Yang Yi","year":"2023","unstructured":"Yi Yang, Yixuan Tang, and Kar Yan Tam. 2023. InvestLM: A large language model for investment using financial domain instruction tuning. arXiv preprint arXiv:2309.13064 (2023).","journal-title":"arXiv preprint arXiv:2309.13064"},{"key":"e_1_3_3_65_2","article-title":"FinBERT: A pretrained language model for financial communications","author":"Yang Yi","year":"2020","unstructured":"Yi Yang, Mark Christopher Siy Uy, and Allen Huang. 2020. FinBERT: A pretrained language model for financial communications. arXiv preprint arXiv:2006.08097 (2020).","journal-title":"arXiv preprint arXiv:2006.08097"},{"key":"e_1_3_3_66_2","article-title":"Chatdoctor: A medical chat model fine-tuned on Llama model using medical domain knowledge","author":"Yunxiang Li","year":"2023","unstructured":"Li Yunxiang, Li Zihan, Zhang Kai, Dan Ruilong, and Zhang You. 2023. Chatdoctor: A medical chat model fine-tuned on Llama model using medical domain knowledge. arXiv preprint arXiv:2303.14070 (2023).","journal-title":"arXiv preprint arXiv:2303.14070"},{"key":"e_1_3_3_67_2","article-title":"Instruct-finGPT: Financial sentiment analysis by instruction tuning of general-purpose large language models","author":"Zhang Boyu","year":"2023","unstructured":"Boyu Zhang, Hongyang Yang, and Xiao-Yang Liu. 2023. Instruct-finGPT: Financial sentiment analysis by instruction tuning of general-purpose large language models. arXiv preprint arXiv:2306.12659 (2023).","journal-title":"arXiv preprint arXiv:2306.12659"},{"key":"e_1_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604237.3626866"},{"key":"e_1_3_3_69_2","article-title":"Instruction tuning for large language models: A survey","author":"Zhang Shengyu","year":"2023","unstructured":"Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et\u00a0al. 2023. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792 (2023).","journal-title":"arXiv preprint arXiv:2308.10792"},{"key":"e_1_3_3_70_2","article-title":"Sentiment analysis in the era of large language models: A reality check","author":"Zhang Wenxuan","year":"2023","unstructured":"Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing. 2023. Sentiment analysis in the era of large language models: A reality check. arXiv preprint arXiv:2305.15005 (2023).","journal-title":"arXiv preprint arXiv:2305.15005"},{"key":"e_1_3_3_71_2","article-title":"Multi-task instruction tuning of Llama for specific scenarios: A preliminary study on writing assistance","author":"Zhang Yue","year":"2023","unstructured":"Yue Zhang, Leyang Cui, Deng Cai, Xinting Huang, Tao Fang, and Wei Bi. 2023. Multi-task instruction tuning of Llama for specific scenarios: A preliminary study on writing assistance. arXiv preprint arXiv:2305.13225 (2023).","journal-title":"arXiv preprint arXiv:2305.13225"},{"key":"e_1_3_3_72_2","article-title":"Can ChatGPT understand too? A comparative study on chatGPT and fine-tuned BERT","author":"Zhong Qihuang","year":"2023","unstructured":"Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023. Can ChatGPT understand too? A comparative study on chatGPT and fine-tuned BERT. arXiv preprint arXiv:2302.10198 (2023).","journal-title":"arXiv preprint arXiv:2302.10198"},{"key":"e_1_3_3_73_2","article-title":"Multilingual machine translation with large language models: Empirical results and analysis","author":"Zhu Wenhao","year":"2023","unstructured":"Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. 2023. Multilingual machine translation with large language models: Empirical results and analysis. arXiv preprint arXiv:2304.04675 (2023).","journal-title":"arXiv preprint arXiv:2304.04675"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706119","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706119","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:03Z","timestamp":1750295883000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,8]]},"references-count":72,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3,31]]}},"alternative-id":["10.1145\/3706119"],"URL":"https:\/\/doi.org\/10.1145\/3706119","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"value":"2158-656X","type":"print"},{"value":"2158-6578","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,8]]},"assertion":[{"value":"2023-12-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}