{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:08:17Z","timestamp":1755907697546,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":47,"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:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,14]]},"DOI":"10.1145\/3677052.3698597","type":"proceedings-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T06:38:06Z","timestamp":1731566286000},"page":"591-599","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9007-3255","authenticated-orcid":false,"given":"Nicole","family":"Cho","sequence":"first","affiliation":[{"name":"J.P. Morgan, US"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-3018","authenticated-orcid":false,"given":"Nishan","family":"Srishankar","sequence":"additional","affiliation":[{"name":"J.P. Morgan, US"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4501-3710","authenticated-orcid":false,"given":"Lucas","family":"Cecchi","sequence":"additional","affiliation":[{"name":"J.P. Morgan, US"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5516-262X","authenticated-orcid":false,"given":"William","family":"Watson","sequence":"additional","affiliation":[{"name":"J.P. Morgan, US"}]}],"member":"320","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"1984. Edgar. https:\/\/www.sec.gov\/edgar."},{"key":"e_1_3_2_1_2_1","unstructured":"1984. IAPD. https:\/\/adviserinfo.sec.gov\/."},{"key":"e_1_3_2_1_3_1","unstructured":"Dogu Araci. 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arxiv:1908.10063\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/1908.10063"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591875"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.62051\/ijgem.v2n3.08"},{"key":"e_1_3_2_1_6_1","unstructured":"Tianle Cai Xuezhi Wang Tengyu Ma Xinyun Chen and Denny Zhou. 2024. Large Language Models as Tool Makers. arxiv:2305.17126\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2305.17126"},{"key":"e_1_3_2_1_7_1","unstructured":"Matthijs Douze Alexandr Guzhva Chengqi Deng Jeff Johnson Gergely Szilvasy Pierre-Emmanuel Mazar\u00e9 Maria Lomeli Lucas Hosseini and Herv\u00e9 J\u00e9gou. 2024. The Faiss library. arxiv:2401.08281\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2401.08281"},{"key":"e_1_3_2_1_8_1","unstructured":"Xu Huang Weiwen Liu Xiaolong Chen Xingmei Wang Hao Wang Defu Lian Yasheng Wang Ruiming Tang and Enhong Chen. 2024. Understanding the planning of LLM agents: A survey. arxiv:2402.02716\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2402.02716"},{"key":"e_1_3_2_1_9_1","unstructured":"Rolf Jagerman Honglei Zhuang Zhen Qin Xuanhui Wang and Michael Bendersky. 2023. Query Expansion by Prompting Large Language Models. arxiv:2305.03653\u00a0[cs.IR] https:\/\/arxiv.org\/abs\/2305.03653"},{"key":"e_1_3_2_1_10_1","unstructured":"Albert\u00a0Q. Jiang Alexandre Sablayrolles Antoine Roux Arthur Mensch Blanche Savary Chris Bamford Devendra\u00a0Singh Chaplot Diego de\u00a0las Casas Emma\u00a0Bou Hanna Florian Bressand Gianna Lengyel Guillaume Bour Guillaume Lample L\u00e9lio\u00a0Renard Lavaud Lucile Saulnier Marie-Anne Lachaux Pierre Stock Sandeep Subramanian Sophia Yang Szymon Antoniak Teven\u00a0Le Scao Th\u00e9ophile Gervet Thibaut Lavril Thomas Wang Timoth\u00e9e Lacroix and William\u00a0El Sayed. 2024. Mixtral of Experts. arxiv:2401.04088\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2401.04088"},{"key":"e_1_3_2_1_11_1","unstructured":"Xuhui Jiang Chengjin Xu Yinghan Shen Xun Sun Lumingyuan Tang Saizhuo Wang Zhongwu Chen Yuanzhuo Wang and Jian Guo. 2023. On the Evolution of Knowledge Graphs: A Survey and Perspective. arxiv:2310.04835\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2310.04835"},{"key":"e_1_3_2_1_12_1","unstructured":"Aoran Jiao Tanmay\u00a0P. Patel Sanjmi Khurana Anna-Mariya Korol Lukas Brunke Vivek\u00a0K. Adajania Utku Culha Siqi Zhou and Angela\u00a0P. Schoellig. 2023. Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design. arxiv:2312.01059\u00a0[cs.RO] https:\/\/arxiv.org\/abs\/2312.01059"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_3_2_1_14_1","unstructured":"Jean Lee Nicholas Stevens Soyeon\u00a0Caren Han and Minseok Song. 2024. A Survey of Large Language Models in Finance (FinLLMs). arxiv:2402.02315\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2402.02315"},{"key":"e_1_3_2_1_15_1","unstructured":"Cheng Li Jindong Wang Yixuan Zhang Kaijie Zhu Wenxin Hou Jianxun Lian Fang Luo Qiang Yang and Xing Xie. 2023. Large Language Models Understand and Can be Enhanced by Emotional Stimuli. arxiv:2307.11760\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2307.11760"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160591"},{"key":"e_1_3_2_1_17_1","unstructured":"Xukun Liu Zhiyuan Peng Xiaoyuan Yi Xing Xie Lirong Xiang Yuchen Liu and Dongkuan Xu. 2024. ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph. arxiv:2403.00839\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2403.00839"},{"key":"e_1_3_2_1_18_1","volume-title":"FinGPT: Democratizing Internet-scale Data for Financial Large Language Models. arXiv preprint arXiv:2307.10485","author":"Liu Xiao-Yang","year":"2023","unstructured":"Xiao-Yang Liu, Guoxuan Wang, and Daochen Zha. 2023. FinGPT: Democratizing Internet-scale Data for Financial Large Language Models. arXiv preprint arXiv:2307.10485 (2023)."},{"key":"e_1_3_2_1_19_1","volume-title":"Leeroo Orchestrator: Elevating LLMs Performance Through Model Integration. arxiv:2401.13979\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2401.13979","author":"Mohammadshahi Alireza","year":"2024","unstructured":"Alireza Mohammadshahi, Ali Shaikh, and Majid Yazdani. 2024. Leeroo Orchestrator: Elevating LLMs Performance Through Model Integration. arxiv:2401.13979\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2401.13979"},{"key":"e_1_3_2_1_20_1","unstructured":"Reiichiro Nakano Jacob Hilton Suchir Balaji Jeff Wu Long Ouyang Christina Kim Christopher Hesse Shantanu Jain Vineet Kosaraju William Saunders Xu Jiang Karl Cobbe Tyna Eloundou Gretchen Krueger Kevin Button Matthew Knight Benjamin Chess and John Schulman. 2022. WebGPT: Browser-assisted question-answering with human feedback. arxiv:2112.09332\u00a0[cs.CL]"},{"key":"e_1_3_2_1_21_1","unstructured":"Yuqi Nie Yaxuan Kong Xiaowen Dong John\u00a0M. Mulvey H.\u00a0Vincent Poor Qingsong Wen and Stefan Zohren. 2024. A Survey of Large Language Models for Financial Applications: Progress Prospects and Challenges. arxiv:2406.11903\u00a0[q-fin.GN] https:\/\/arxiv.org\/abs\/2406.11903"},{"key":"e_1_3_2_1_22_1","volume-title":"Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474","author":"Nijkamp Erik","year":"2022","unstructured":"Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022. Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474 (2022)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Vishal Pallagani Kaushik Roy Bharath Muppasani Francesco Fabiano Andrea Loreggia Keerthiram Murugesan Biplav Srivastava Francesca Rossi Lior Horesh and Amit Sheth. 2024. On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). arxiv:2401.02500\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2401.02500","DOI":"10.1609\/icaps.v34i1.31503"},{"key":"e_1_3_2_1_24_1","volume-title":"Expert Router: Orchestrating Efficient Language Model Inference through Prompt Classification. arxiv:2404.15153\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2404.15153","author":"Pichlmeier Josef","year":"2024","unstructured":"Josef Pichlmeier, Philipp Ross, and Andre Luckow. 2024. Expert Router: Orchestrating Efficient Language Model Inference through Prompt Classification. arxiv:2404.15153\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2404.15153"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10214-4"},{"key":"e_1_3_2_1_26_1","unstructured":"Yujia Qin Shihao Liang Yining Ye Kunlun Zhu Lan Yan Yaxi Lu Yankai Lin Xin Cong Xiangru Tang Bill Qian Sihan Zhao Lauren Hong Runchu Tian Ruobing Xie Jie Zhou Mark Gerstein Dahai Li Zhiyuan Liu and Maosong Sun. 2023. ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs. arxiv:2307.16789\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2307.16789"},{"key":"e_1_3_2_1_27_1","volume-title":"Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs. arxiv:2402.16713\u00a0[cs.MA] https:\/\/arxiv.org\/abs\/2402.16713","author":"Rasal Sumedh","year":"2024","unstructured":"Sumedh Rasal and E.\u00a0J. Hauer. 2024. Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs. arxiv:2402.16713\u00a0[cs.MA] https:\/\/arxiv.org\/abs\/2402.16713"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.9833"},{"key":"e_1_3_2_1_29_1","unstructured":"Louis Rosenberg Gregg Willcox Hans Schumann Miles Bader Ganesh Mani Kokoro Sagae Devang Acharya Yuxin Zheng Andrew Kim and Jialing Deng. 2023. Conversational Swarm Intelligence a Pilot Study. arxiv:2309.03220\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2309.03220"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Louis Rosenberg Gregg Willcox Hans Schumann and Ganesh Mani. 2024. Towards Collective Superintelligence: Amplifying Group IQ using Conversational Swarms. arxiv:2401.15109\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2401.15109","DOI":"10.5220\/0012687500003690"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Tobias Schimanski Jingwei Ni Mathias Kraus Elliott Ash and Markus Leippold. 2024. Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering. arxiv:2402.08277\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2402.08277","DOI":"10.2139\/ssrn.4728973"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Sanjay Subramanian Medhini Narasimhan Kushal Khangaonkar Kevin Yang Arsha Nagrani Cordelia Schmid Andy Zeng Trevor Darrell and Dan Klein. 2023. Modular Visual Question Answering via Code Generation. arxiv:2306.05392\u00a0[cs.CL]","DOI":"10.18653\/v1\/2023.acl-short.65"},{"key":"e_1_3_2_1_33_1","volume-title":"Securities Exchange Act of","author":"Congress U.S.","year":"1934","unstructured":"U.S. Congress. 1934. Securities Exchange Act of 1934. https:\/\/www.govinfo.gov\/content\/pkg\/COMPS-1884\/pdf\/COMPS-1884.pdf. Codified at 15 U.S.C. \u00a7 78a et seq.."},{"key":"e_1_3_2_1_34_1","volume-title":"Investment Advisers Act of","author":"Congress U.S.","year":"1940","unstructured":"U.S. Congress. 1940. Investment Advisers Act of 1940. https:\/\/www.govinfo.gov\/content\/pkg\/COMPS-1877\/pdf\/COMPS-1877.pdf. Codified at 15 U.S.C. \u00a7 80b-1 et seq.."},{"key":"e_1_3_2_1_35_1","volume-title":"Investment Company Act of","author":"Congress U.S.","year":"1940","unstructured":"U.S. Congress. 1940. Investment Company Act of 1940. https:\/\/www.govinfo.gov\/content\/pkg\/COMPS-1876\/pdf\/COMPS-1876.pdf. Codified at 15 U.S.C. \u00a7 80a-1 et seq.."},{"key":"e_1_3_2_1_36_1","unstructured":"Karthik Valmeekam Matthew Marquez Sarath Sreedharan and Subbarao Kambhampati. 2023. On the Planning Abilities of Large Language Models : A Critical Investigation. arxiv:2305.15771\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2305.15771"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Xu Wang Cheng Li Yi Chang Jindong Wang and Yuan Wu. 2024. NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli. arxiv:2405.02814\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2405.02814","DOI":"10.24963\/ijcai.2024\/719"},{"key":"e_1_3_2_1_38_1","unstructured":"William Watson and Nicole Cho. 2024. HalluciBot: Is There No Such Thing as a Bad Question?arxiv:2404.12535v1\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2404.12535v1"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.442"},{"key":"e_1_3_2_1_40_1","unstructured":"William Watson Nicole Cho and Nishan Srishankar. 2024. Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination Rewriting Ranking and Routing. arxiv:2404.12535\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2404.12535"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383455.3422520"},{"key":"e_1_3_2_1_42_1","unstructured":"William Watson and Lawrence Yong. 2024. Directed Criteria Citation Recommendation and Ranking Through Link Prediction. arxiv:2403.18855\u00a0[cs.SI] https:\/\/arxiv.org\/abs\/2403.18855"},{"key":"e_1_3_2_1_43_1","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:2303.17564\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2303.17564"},{"key":"e_1_3_2_1_44_1","volume-title":"FinGPT: Open-Source Financial Large Language Models. arXiv preprint arXiv:2306.06031","author":"Yang Hongyang","year":"2023","unstructured":"Hongyang Yang, Xiao-Yang Liu, and Christina\u00a0Dan Wang. 2023. FinGPT: Open-Source Financial Large Language Models. arXiv preprint arXiv:2306.06031 (2023)."},{"key":"e_1_3_2_1_45_1","unstructured":"Zhen Zeng William Watson Nicole Cho Saba Rahimi Shayleen Reynolds Tucker Balch and Manuela Veloso. 2024. FlowMind: Automatic Workflow Generation with LLMs. arxiv:2404.13050\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2404.13050"},{"key":"e_1_3_2_1_46_1","volume-title":"Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models. arXiv preprint arXiv:2306.12659","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)."},{"key":"e_1_3_2_1_47_1","unstructured":"Ran Zmigrod Dongsheng Wang Mathieu Sibue Yulong Pei Petr Babkin Ivan Brugere Xiaomo Liu Nacho Navarro Antony Papadimitriou William Watson Zhiqiang Ma Armineh Nourbakhsh and Sameena Shah. 2024. BuDDIE: A Business Document Dataset for Multi-task Information Extraction. arxiv:2404.04003\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2404.04003"}],"event":{"name":"ICAIF '24: 5th ACM International Conference on AI in Finance","acronym":"ICAIF '24","location":"Brooklyn NY USA"},"container-title":["Proceedings of the 5th ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677052.3698597","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3677052.3698597","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:13:43Z","timestamp":1755882823000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677052.3698597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":47,"alternative-id":["10.1145\/3677052.3698597","10.1145\/3677052"],"URL":"https:\/\/doi.org\/10.1145\/3677052.3698597","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"}}]}}