Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA), while also revolutionizing the conventional task-oriented dialogue (TOD) paradigm.
The Society of Mind
Marvin Minsky. 1986 · 1986
Earlier work this paper cites.
PARADISE: A framework for evaluating spoken dialogue agents
Marilyn A. Walker, Diane J. Litman, Candace A. Kamm, and Alicia Abella. 1997 · 1997
Earlier work this paper cites.
Talking to machines (statistically speaking)
Steve Young. 2002 · 2002
Earlier work this paper cites.
Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, et al. 2018 · 2018
Earlier work this paper cites.
Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset
Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. 2020 · 2020
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
InstructDial: Improving zero and few-shot generalization in dialogue through instruction tuning
Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, and Jeffrey Bigham. 2022 · 2022
Earlier work this paper cites.
In-context learning for few-shot dialogue state tracking
Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, Tao Yu, Noah A. Smith, and Mari Ostendorf. 2022 · 2022
Earlier work this paper cites.
Multi-task pre-training for plug-and-play task-oriented dialogue system
Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, and Yi Zhang. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
MultiWOZ 2.4: A multi-domain task-oriented dialogue dataset with essential annotation corrections to improve state tracking evaluation
Fanghua Ye, Jarana Manotumruksa, and Emine Yilmaz. 2022 · 2022
Earlier work this paper cites.
InstructTODS: Large language models for end-to-end task-oriented dialogue systems
Willy Chung, Samuel Cahyawijaya, Bryan Wilie, Holy Lovenia, and Pascale Fung. 2023 · 2023
Earlier work this paper cites.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
Earlier work this paper cites.
Towards LLM-driven dialogue state tracking
Yujie Feng, Zexin Lu, Bo Liu, Liming Zhan, and Xiao-Ming Wu. 2023 · 2023
Cited alongside, same era.
Are large language models all you need for task-oriented dialogue?
Vojtěch Hudeček and Ondrej Dusek. 2023 · 2023
Cited alongside, same era.
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 al. 2023 · 2023
Cited alongside, same era.
API-bank: A comprehensive benchmark for tool-augmented LLMs
Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. 2023 · 2023
Cited alongside, same era.
International workshop on multimodal learning - 2023 theme: Multimodal learning with foundation models
Yuan Ling, Fanyou Wu, Shujing Dong, Yarong Feng, George Karypis, and Chandan K. Reddy. 2023 · 2023
Cited alongside, same era.
Large language models as zero-shot dialogue state tracker through function calling
Zekun Li, Zhiyu Zoey Chen, Mike Ross, Patrick Huber, Seungwhan Moon, Zhaojiang Lin, Xin Luna Dong, Adithya Sagar, Xifeng Yan, and Paul A. Crook. 2024 · 2024
Later among the works it cites.
Hammer: Robust function-calling for on-device language models via function masking
Qiqiang Lin, Muning Wen, Qiuying Peng, Guanyu Nie, Junwei Liao, Jun Wang, Xiaoyun Mo, Jiamu Zhou, Cheng Cheng, Yin Zhao, and Weinan Zhang. 2024 · 2024
Later among the works it cites.
Toolace: Winning the points of llm function calling
Weiwen Liu, Xu Huang, Xingshan Zeng, Xinlong Hao, Shuai Yu, Dexun Li, Shuai Wang, Weinan Gan, Zhengying Liu, Yuanqing Yu, Zezhong Wang, Yuxian Wang, Wu Ning, Yutai Hou, Bin Wang, Chuhan Wu, Xinzhi Wang, Yong Liu, Yasheng Wang, Duyu Tang, Dandan Tu, Lifeng Shang, Xin Jiang, Ruiming Tang, Defu Lian, Qun Liu, and Enhong Chen. 2024 · 2024
Later among the works it cites.
OpenAI, Josh Achiam, et al. 2024 · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Art: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro. 2023 · 2023
Cited alongside, same era.
Gorilla: Large language model connected with massive apis
Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. 2023 · 2023
Cited alongside, same era.
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023 · 2023
Cited alongside, same era.
SGP-TOD: Building task bots effortlessly via schema-guided LLM prompting
Xiaoying Zhang, Baolin Peng, Kun Li, Jingyan Zhou, and Helen Meng. 2023 · 2023
Cited alongside, same era.
Granite-function calling model: Introducing function calling abilities via multi-task learning of granular tasks
Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, G P Shrivatsa Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachindra Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Meza Soria, Dheeraj Sreedhar, Praveen Venkateswaran, Merve Unuvar, David Daniel Cox, Salim Roukos, Luis A. Lastras, and Pavan Kapanipathi. 2024 · 2024
Cited alongside, same era.
Abhimanyu Dubey et al. 2024 · 2024
Cited alongside, same era.
Unsupervised end-to-end task-oriented dialogue with LLMs: The power of the noisy channel
Brendan King and Jeffrey Flanigan. 2024 · 2024
Cited alongside, same era.
Later among the works it cites.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2024 · 2024
Later among the works it cites.
Executable code actions elicit better llm agents
Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. 2024 · 2024
Later among the works it cites.
Rethinking task-oriented dialogue systems: From complex modularity to zero-shot autonomous agent
Heng-Da Xu, Xian-Ling Mao, Puhai Yang, Fanshu Sun, and Heyan Huang. 2024 · 2024
Later among the works it cites.
Berkeley function calling leaderboard
Fanjia Yan, Huanzhi Mao, Charlie Cheng-Jie Ji, Tianjun Zhang, Shishir G. Patil, Ion Stoica, and Joseph E. Gonzalez. 2024 · 2024
Later among the works it cites.
xlam: A family of large action models to empower ai agent systems
Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, et al. 2024 · 2024
Later among the works it cites.
bitsandbytes
BitsAndBytes. 2025 · 2025
Closest in time.
Oumi: an open, end-to-end platform for building large foundation models
Oumi. 2025 · 2025
Closest in time.
Together ai: The ai acceleration cloud
TogetherAI. 2025 · 2025
Closest in time.