{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:29:59Z","timestamp":1755221399222,"version":"3.43.0"},"reference-count":83,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>Task-oriented dialogue (TOD) systems play a vital role in numerous assistance and service scenarios, significantly improving people\u2019s daily lives. Conventionally, a TOD system adheres to a fixed paradigm, where it must first extract user goals and query external databases before it can generate the final response. However, this fixed extract-and-query paradigm is not always optimal for all dialogue turns, which is redundant for the simple turns that do not need external information, and is inadequate for the complex turns that need to interact with the external world multiple times. To address the limitations, in this article, we propose AgentTOD, a novel TOD framework that uses a large language model (LLM) as the intelligent agent to achieve a flexible dialogue paradigm. AgentTOD deprecates the traditional modular architecture (including dialogue state tracking and dialogue policy) by utilizing an LLM as the controller brain to determine when and how to call the provided APIs to obtain external information. It can choose to call APIs any number of times with various parameters until it\u2019s enough to reply to the user. Besides, to train AgentTOD, we construct a large and comprehensive TOD dataset, called TrajsTOD (Trajectories of TODs), which consists of 66k+ user-agent dialogue trajectories converted from eight popular TOD datasets covering 60 domains. TrajsTOD is constructed with minimal dialogue annotations where only the API calling logs are needed and can empower AgentTOD with the general ability to call APIs and generate responses according to the task definition. Extensive experimental results on the MultiWOZ-series and SGD datasets demonstrate AgentTOD has superior performance on TODs as well as a superior adaptability to new task scenarios.<\/jats:p>","DOI":"10.1145\/3745021","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T11:14:20Z","timestamp":1750418060000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AgentTOD: A Task-Oriented Dialogue Agent with a Flexible and Adaptive API Calling Paradigm"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4083-5400","authenticated-orcid":false,"given":"Heng-Da","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6795-2311","authenticated-orcid":false,"given":"Xian-Ling","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7969-855X","authenticated-orcid":false,"given":"Fanshu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9383-4092","authenticated-orcid":false,"given":"Tian-Yi","family":"Che","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6264-0682","authenticated-orcid":false,"given":"Chun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0320-7520","authenticated-orcid":false,"given":"Heyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Daniel Adiwardana Minh-Thang Luong David R. So Jamie Hall Noah Fiedel Romal Thoppilan Zi Yang Apoorv Kulshreshtha Gaurav Nemade Yifeng Lu et al. 2020. Towards a human-like open-domain chatbot. arXiv:2001.09977. Retrieved from https:\/\/arxiv.org\/abs\/2001.09977"},{"key":"e_1_3_1_3_2","unstructured":"Rohan Anil Sebastian Borgeaud Yonghui Wu Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M. Dai Anja Hauth Katie Millican et al. 2023. Gemini: A family of highly capable multimodal models. arXiv:2312.11805. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2312.11805"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/W17-5526"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.464"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.9"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1547"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2021.ACL-LONG.55"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1459"},{"key":"e_1_3_1_10_2","unstructured":"Qian Chen Zhu Zhuo and Wen Wang. 2019. BERT for joint intent classification and slot filling. arXiv:1902.10909. Retrieved from http:\/\/arxiv.org\/abs\/1902.10909"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.601"},{"key":"e_1_3_1_12_2","unstructured":"Aakanksha\u00a0 Chowdhery Sharan\u00a0 Narang Jacob\u00a0 Devlin Maarten\u00a0 Bosma Gaurav\u00a0 Mishra Adam\u00a0 Roberts Paul\u00a0 Barham Hyung Won Chung Charles\u00a0 Sutton Sebastian\u00a0 Gehrmann et al. 2022. PaLM: Scaling language modeling with pathways. arXiv:2204.02311. Retrieved from https:\/\/arxiv.org\/abs\/2204.02311"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_1_14_2","volume-title":"Advances in Neural Information Processing Systems","author":"Dong Li","year":"2019","unstructured":"Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified language model pre-training for natural language understanding and generation. In Advances in Neural Information Processing Systems. H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/c20bb2d9a50d5ac1f713f8b34d9aac5a-Paper.pdf"},{"key":"e_1_3_1_15_2","first-page":"9147","volume-title":"Proceedings of the 31st International Conference on Computational Linguistics","author":"Dong Wenjie","year":"2025","unstructured":"Wenjie Dong, Sirong Chen, and Yan Yang. 2025. ProTOD: Proactive task-oriented dialogue system based on large language model. In Proceedings of the 31st International Conference on Computational Linguistics. Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert (Eds.), Association for Computational Linguistics, 9147\u20139164. Retrieved from https:\/\/aclanthology.org\/2025.coling-main.614\/"},{"key":"e_1_3_1_16_2","first-page":"422","volume-title":"Proceedings of the 12th Language Resources and Evaluation Conference","author":"Eric Mihail","year":"2020","unstructured":"Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, and Dilek Hakkani-Tur. 2020. MultiWOZ 2.1: A consolidated multi-domain dialogue dataset with state corrections and state tracking baselines. In Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, 422\u2013428. Retrieved from https:\/\/aclanthology.org\/2020.lrec-1.53"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/W17-5506"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21320"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.196"},{"key":"e_1_3_1_20_2","first-page":"20179","volume-title":"Advances in Neural Information Processing Systems","author":"Hosseini-Asl Ehsan","year":"2020","unstructured":"Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, and Richard Socher. 2020. A simple language model for task-oriented dialogue. In Advances in Neural Information Processing Systems. H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 20179\u201320191. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/e946209592563be0f01c844ab2170f0c-Paper.pdf"},{"key":"e_1_3_1_21_2","volume-title":"International Conference on Learning Representations","author":"Hu Edward J.","year":"2022","unstructured":"Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-emnlp.193"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/357417.357420"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.53"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.344"},{"key":"e_1_3_1_26_2","unstructured":"Angeliki Lazaridou Elena Gribovskaya Wojciech Stokowiec and Nikolai Grigorev. 2022. Internet-augmented language models through few-shot prompting for open-domain question answering. arXiv:2203.05115. Retrieved from https:\/\/arxiv.org\/abs\/2203.05115"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/P19-3011"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.112"},{"key":"e_1_3_1_29_2","first-page":"707","article-title":"Binary codes capable of correcting deletions, insertions, and reversals","volume":"10","author":"Levenshtein Vladimir I.","year":"1965","unstructured":"Vladimir I. Levenshtein. 1965. Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics. Doklady 10 (1965), 707\u2013710. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:60827152","journal-title":"Soviet physics. Doklady"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","unstructured":"Yaobo Liang Chenfei Wu Ting Song Wenshan Wu Yan Xia Yu Liu Yang Ou Shuai Lu Lei Ji Shaoguang Mao et al. 2023. TaskMatrix.AI: Completing tasks by connecting foundation models with millions of APIs. arXiv:2303.16434. Retrieved from https:\/\/arxiv.org\/abs\/2303.16434","DOI":"10.34133\/icomputing.0063"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.622"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.448"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.273"},{"key":"e_1_3_1_34_2","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. arXiv:1711.05101. Retrieved from https:\/\/arxiv.org\/abs\/1711.05101"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-5921"},{"key":"e_1_3_1_36_2","unstructured":"Meta. 2024. Introducing Meta Llama 3: The Most Capable Openly Available LLM to Date. Retrieved May 13 2024 from https:\/\/ai.meta.com\/blog\/meta-llama-3\/"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-industry.36"},{"key":"e_1_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Adib Mosharrof M. H. Maqbool and A. B. Siddique. 2023. Zero-shot generalizable end-to-end task-oriented dialog system using context summarization and domain schema. arXiv:2303.16252. Retrieved from https:\/\/arxiv.org\/abs\/2303.16252","DOI":"10.32473\/flairs.36.133072"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/P17-1163"},{"key":"e_1_3_1_40_2","unstructured":"Yohei Nakajima. 2023. BabyAGI. Retrieved from https:\/\/github.com\/yoheinakajima\/babyagi"},{"key":"e_1_3_1_41_2","unstructured":"Jinjie Ni Tom Young Vlad Pandelea Fuzhao Xue and Erik Cambria. 2021. Recent advances in deep learning based dialogue systems: A systematic survey. arXiv:2105.04387. Retrieved from https:\/\/doi.org\/10.48550\/ARXIV.2105.04387"},{"key":"e_1_3_1_42_2","unstructured":"OpenAI. 2022. Introducing ChatGPT. Retrieved August 13 2023 from https:\/\/openai.com\/blog\/chatgpt"},{"key":"e_1_3_1_43_2","unstructured":"OpenAI. 2023. GPT-4 technical report. arXiv:2303.08774. Retrieved from https:\/\/arxiv.org\/abs\/2303.08774"},{"key":"e_1_3_1_44_2","unstructured":"OpenAI. 2023. Models - GPT-3.5. Retrieved August 13 2023 from https:\/\/platform.openai.com\/docs\/models\/gpt-3-5"},{"key":"e_1_3_1_45_2","first-page":"27730","volume-title":"Advances in Neural Information Processing Systems","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 al. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems. S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 27730\u201327744. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/b1efde53be364a73914f58805a001731-Paper-Conference.pdf"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"e_1_3_1_47_2","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga et al. 2019. PyTorch: An imperative style high-performance deep learning library. arXiv:1912.01703. Retrieved from https:\/\/arxiv.org\/abs\/1912.01703"},{"key":"e_1_3_1_48_2","unstructured":"Shishir G. Patil Tianjun Zhang Xin Wang and Joseph E. Gonzalez. 2023. Gorilla: Large language model connected with massive APIs. arXiv:2305.15334. Retrieved from https:\/\/arxiv.org\/abs\/2305.15334"},{"key":"e_1_3_1_49_2","unstructured":"Baolin Peng Michel Galley Pengcheng He Chris Brockett Lars Liden Elnaz Nouri Zhou Yu Bill Dolan and Jianfeng Gao. 2022. GODEL: Large-scale pre-training for goal-directed dialog. arXiv:2206.11309. Retrieved from https:\/\/arxiv.org\/abs\/2206.11309"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00399"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1203"},{"key":"e_1_3_1_52_2","doi-asserted-by":"crossref","unstructured":"Ofir Press Muru Zhang Sewon Min Ludwig Schmidt Noah A. Smith and Mike Lewis. 2023. Measuring and narrowing the compositionality gap in language models. arXiv:2210.03350. Retrieved from https:\/\/arxiv.org\/abs\/2210.03350","DOI":"10.18653\/v1\/2023.findings-emnlp.378"},{"key":"e_1_3_1_53_2","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei and Ilya Sutskever. 2018. Language Models Are Unsupervised Multitask Learners. Retrieved from https:\/\/d4mucfpksywv.cloudfront.net\/better-language-models\/language-models.pdf"},{"issue":"140","key":"e_1_3_1_54_2","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21, 140 (2020), 1\u201367. Retrieved from http:\/\/jmlr.org\/papers\/v21\/20-074.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6394"},{"key":"e_1_3_1_56_2","unstructured":"ReworkdAI. 2023. AgentGPT. Retrieved from https:\/\/github.com\/reworkd\/AgentGPT"},{"key":"e_1_3_1_57_2","unstructured":"Toran Bruce Richards. 2023. Auto-GPT. Retrieved from https:\/\/github.com\/Significant-Gravitas\/Auto-GPT"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.24"},{"key":"e_1_3_1_59_2","unstructured":"Victor Sanh Lysandre Debut Julien Chaumond and Thomas Wolf. 2019. DistilBERT a distilled version of BERT: Smaller faster cheaper and lighter. arXiv:1910.01108. Retrieved from http:\/\/arxiv.org\/abs\/1910.01108"},{"key":"e_1_3_1_60_2","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023 (NeurIPS\u00a0\u201923)","author":"Schick Timo","year":"2023","unstructured":"Timo Schick, Jane Dwivedi-Yu, Roberto Dess\u00ec, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023 (NeurIPS\u00a0\u201923). Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (Eds.). Retrieved from http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/d842425e4bf79ba039352da0f658a906-Abstract-Conference.html"},{"key":"e_1_3_1_61_2","unstructured":"Pararth Shah Dilek Hakkani-T\u00fcr Gokhan T\u00fcr Abhinav Rastogi Ankur Bapna Neha Nayak and Larry Heck. 2018. Building a conversational agent overnight with dialogue self-play. arXiv:1801.04871. Retrieved from http:\/\/arxiv.org\/abs\/1801.04871"},{"key":"e_1_3_1_62_2","unstructured":"Yongliang Shen Kaitao Song Xu Tan Dongsheng Li Weiming Lu and Yueting Zhuang. 2023. HuggingGPT: Solving AI tasks with ChatGPT and its friends in hugging face. arXiv:2303.17580. Retrieved from https:\/\/arxiv.org\/abs\/2303.17580"},{"key":"e_1_3_1_63_2","unstructured":"Raphael Shu Elman Mansimov Tamer Alkhouli Nikolaos Pappas Salvatore Romeo Arshit Gupta Saab Mansour Yi Zhang and Dan Roth. 2022. Dialog2API: Task-oriented dialogue with API description and example programs. arXiv:2212.09946. Retrieved from https:\/\/arxiv.org\/abs\/2212.09946"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.319"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-naacl.166"},{"key":"e_1_3_1_66_2","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288. Retrieved from https:\/\/arxiv.org\/abs\/2307.09288"},{"key":"e_1_3_1_67_2","volume-title":"Proceedings of the 7th International Conference on Learning Representations (ICLR \u201919)","author":"Wang Alex","year":"2019","unstructured":"Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 7th International Conference on Learning Representations (ICLR \u201919). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=rJ4km2R5t7"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.638"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-40231-1"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/E17-1042"},{"key":"e_1_3_1_71_2","doi-asserted-by":"crossref","unstructured":"Thomas Wolf Lysandre Debut Victor Sanh Julien Chaumond Clement Delangue Anthony Moi Pierric Cistac Tim Rault R\u00e9mi Louf Morgan Funtowicz et al. 2020. HuggingFace\u2019s transformers: State-of-the-art natural language processing. arXiv:1910.03771. Retrieved from https:\/\/arxiv.org\/abs\/1910.03771","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1078"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.152"},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","unstructured":"Yunyi Yang Yunhao Li and Xiaojun Quan. 2021. UBAR: Towards fully end-to-end task-oriented dialog systems with GPT-2. arXiv:2012.03539. Retrieved from https:\/\/arxiv.org\/abs\/2012.03539","DOI":"10.1609\/aaai.v35i16.17674"},{"key":"e_1_3_1_75_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Yao Shunyu","year":"2023","unstructured":"Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. 2023. ReAct: Synergizing reasoning and acting in language models. In Proceedings of the 11th International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=WE_vluYUL-X"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.119"},{"key":"e_1_3_1_77_2","doi-asserted-by":"crossref","unstructured":"Xiaoxue Zang Abhinav Rastogi Srinivas Sunkara Raghav Gupta Jianguo Zhang and Jindong Chen. 2020. MultiWOZ 2.2: A dialogue dataset with additional annotation corrections and state tracking baselines. arXiv:2007.12720. Retrieved from https:\/\/arxiv.org\/abs\/2007.12720","DOI":"10.18653\/v1\/2020.nlp4convai-1.13"},{"key":"e_1_3_1_78_2","unstructured":"Aohan Zeng Mingdao Liu Rui Lu Bowen Wang Xiao Liu Yuxiao Dong and Jie Tang. 2023. AgentTuning: Enabling generalized agent abilities for LLMs. arXiv:2310.12823. Retrieved from https:\/\/arxiv.org\/abs\/2310.12823"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.891"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-demos.30"},{"key":"e_1_3_1_81_2","unstructured":"Zheng Zhang Ryuichi Takanobu Qi Zhu Minlie Huang and Xiaoyan Zhu. 2020. Recent advances and challenges in task-oriented dialog system. arXiv:2003.07490. Retrieved from https:\/\/arxiv.org\/abs\/2003.07490"},{"key":"e_1_3_1_82_2","unstructured":"Jeffrey Zhao Raghav Gupta Yuan Cao Dian Yu Mingqiu Wang Harrison Lee Abhinav Rastogi Izhak Shafran and Yonghui Wu. 2022. Description-driven task-oriented dialog modeling. arXiv:2201.08904. Retrieved from https:\/\/arxiv.org\/abs\/2201.08904"},{"key":"e_1_3_1_83_2","unstructured":"Wangchunshu Zhou Yuchen Eleanor Jiang Long Li Jialong Wu Tiannan Wang Shi Qiu Jintian Zhang Jing Chen Ruipu Wu Shuai Wang et al. 2023. Agents: An open-source framework for autonomous language agents. arXiv:2309.07870. Retrieved from https:\/\/arxiv.org\/abs\/2309.07870"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2020.ACL-DEMOS.19"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3745021","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T17:55:38Z","timestamp":1754675738000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3745021"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,8]]},"references-count":83,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9,30]]}},"alternative-id":["10.1145\/3745021"],"URL":"https:\/\/doi.org\/10.1145\/3745021","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"type":"print","value":"1046-8188"},{"type":"electronic","value":"1558-2868"}],"subject":[],"published":{"date-parts":[[2025,8,8]]},"assertion":[{"value":"2024-10-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}