{"type":"ScholarlyArticle","title":"Natural Language Decomposition and Interpretation of Complex Utterances","authors":[{"name":"Harsh Jhamtani","slug":"harsh-jhamtani","given":"Harsh","family":"Jhamtani","affiliation":""},{"name":"Hao Fang","slug":"hao-fang","given":"Hao","family":"Fang","affiliation":""},{"name":"Patrick Xia","slug":"patrick-xia","given":"Patrick","family":"Xia","affiliation":""},{"name":"Eran Levy","slug":"eran-levy","given":"Eran","family":"Levy","affiliation":""},{"name":"Jacob Andreas","slug":"jacob-andreas","given":"Jacob","family":"Andreas","affiliation":""},{"name":"Benjamin Van Durme","slug":"benjamin-van-durme","given":"Benjamin","family":"Van Durme","affiliation":""}],"abstract":"Designing natural language interfaces has historically required collecting supervised data to translate user requests into carefully designed intent representations. This requires enumerating and labeling a long tail of user requests, which is challenging. At the same time, large language models (LLMs) encode knowledge about goals and plans that can help conversational assistants interpret user requests requiring numerous steps to complete. We introduce an approach to handle complex-intent-bearing utterances from a user via a process of hierarchical natural language decomposition and interpretation. Our approach uses a pre-trained language model to decompose a complex utterance into a sequence of simpler natural language steps and interprets each step using the language-to-program model designed for the interface. To test our approach, we collect and release DeCU —a new NL-to-program benchmark to evaluate Decomposition of Complex Utterances. Experiments show that the proposed approach enables the interpretation of complex utterances with almost no complex training data, while outperforming standard few-shot prompting approaches.","doi":"10.24963/ijcai.2024/697","url":"https://ijcai.org/proceedings/2024/697","pdf":"https://ijcai.org/proceedings/2024/0697.pdf","markdown":"https://ijcai.org/proceedings/2024/697.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/697","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":697,"pages":{"first":6306,"last":6314},"keywords":["Natural Language Processing: NLP: Dialogue and interactive systems","Natural Language Processing: NLP: Natural language semantics","Natural Language Processing: NLP: Language grounding"],"videos":[],"references":[{"position":1,"raw":"[Ahn et al., 2022] Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ru- ano, Kyle Jeffrey, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Ret- tinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, and Mengyuan Yan. Do as I can, not as I say: Grounding language in robotic affordances. arXiv: 2204.01691, 2022.","title":"Do as I can, not as I say: Grounding language in robotic affordances","authors":"Anthony Michael Ahn, Noah Brohan, Yevgen Brown, Omar Chebotar, Byron Cortes, Chelsea David, Keerthana Finn, Karol Gopalakrishnan, Alexander Hausman, Daniel Herzog, Jasmine Ho, Julian Hsu, Brian Ibarz, Alex Ichter, Eric Irpan, Rosario Jang, Kyle Jauregui Ruano, Sally Jeffrey, Jesmonth, J Nikhil, Ryan Joshi, Dmitry Julian, Yuheng Kalashnikov, Kuang-Huei Kuang, Sergey Lee, Yao Levine, Linda Lu, Carolina Luu, Peter Parada, Jornell Pastor, Kanishka Quiambao, Jarek Rao, Diego Rettinghouse, Pierre Reyes, Nicolas Sermanet, Clayton Sievers, Alexander Tan, Vincent Toshev, Fei Vanhoucke, Ted Xia, Peng Xiao, Sichun Xu, Mengyuan Xu, Yan","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, Nick Ry- der, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Ad- vances in neural information processing systems, 33:1877- 1901, 2020.","title":"Language models are few-shot learners","authors":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2021] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavar- ian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Eliz- abeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saun- ders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. Eval- uating large language models trained on code. arXiv: 2107.03374, 2021.","title":"Evaluating large language models trained on code","authors":"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob Mcgrew, Dario Amodei, Sam Mccandlish, Ilya Sutskever, Wojciech Zaremba","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Drozdov et al., 2022] Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. Compositional seman- tic parsing with large language models. arXiv: 2209.15003, 2022.","title":"Compositional semantic parsing with large language models","authors":"Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Huang et al., 2022] Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Ma- chine Learning, volume 162 of Proceedings of Machine Learning Research, pages 9118-9147. PMLR, 17-23 Jul 2022.","title":"Language models as zero-shot planners: Extracting actionable knowledge for embodied agents","authors":"Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch","year":"2022","venue":"Proceedings of the 39th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Jiang et al., 2023] Xue Jiang, Yihong Dong, Lecheng Wang, Qiwei Shang, and Ge Li. Self-planning code generation with large language model. arXiv: 2303.06689, 2023.","title":"Self-planning code generation with large language model","authors":"Xue Jiang, Yihong Dong, Lecheng Wang, Qiwei Shang, Ge Li","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Khot et al., 2021] Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, and Ashish Sabharwal. Text modular networks: Learning to decompose tasks in the language of existing models. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Belt- agy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 Con- ference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, NAACL-HLT 2021, Online, June 6-11, 2021, pages 1264-1279. Association for Computational Linguis- tics, 2021.","title":"Text modular networks: Learning to decompose tasks in the language of existing models","authors":"Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal","year":"2021","venue":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Khot et al., 2022] Tushar Khot, Harsh Trivedi, Matthew Fin- layson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. Decomposed prompting: A modular approach for solving complex tasks. arXiv: 2210.02406, 2022.","title":"Decomposed prompting: A modular approach for solving complex tasks","authors":"Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li et al., 2021] Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, and Yashar Mehdad. MTOP: A comprehensive multilingual task-oriented semantic parsing benchmark. In Paola Merlo, Jörg Tiedemann, and Reut Tsarfaty, editors, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, pages 2950-2962. Association for Computational Linguistics, 2021. [OpenAI, 2023] OpenAI. Gpt-4 technical report, 2023.","title":"MTOP: A comprehensive multilingual task-oriented semantic parsing benchmark","authors":"Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, Yashar Mehdad","year":"2021","venue":"Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Paranjape et al., 2023] Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro. Art: Automatic multi-step reasoning and tool-use for large language models. arXiv preprint arXiv:2303.09014, 2023.","title":"Art: Automatic multi-step reasoning and tool-use for large language models","authors":"Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, Marco Tulio, Ribeiro","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Puig et al., 2018] Xavier Puig, Kevin Ra, Marko Boben, Jia- man Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba. Virtualhome: Simulating household activities via programs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8494-8502, 2018.","title":"Virtualhome: Simulating household activities via programs","authors":"Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, Antonio Torralba","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Roy et al., 2022] Subhro Roy, Sam Thomson, Tongfei Chen, Richard Shin, Adam Pauls, Jason Eisner, and Benjamin Van Durme. BenchCLAMP: A benchmark for evaluating lan- guage models on semantic parsing. arXiv: 2206.10668, 2022.","title":"BenchCLAMP: A benchmark for evaluating language models on semantic parsing","authors":"Subhro Roy, Sam Thomson, Tongfei Chen, Richard Shin, Adam Pauls, Jason Eisner, Benjamin Van Durme","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Rubin et al., 2022] Ohad Rubin, Jonathan Herzig, and Jonathan Berant. Learning to retrieve prompts for in- context learning. In Marine Carpuat, Marie-Catherine de Marneffe, and Iván Vladimir Meza Ruíz, editors, Pro- ceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022, pages 2655-2671. Associ- ation for Computational Linguistics, 2022.","title":"Learning to retrieve prompts for incontext learning","authors":"Ohad Rubin, Jonathan Herzig, Jonathan Berant","year":"2022","venue":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Sharma et al., 2022] Pratyusha Sharma, Antonio Torralba, and Jacob Andreas. Skill induction and planning with latent language. In Proceedings of the Annual Association for Computational Linguistics, 2022.","title":"Skill induction and planning with latent language","authors":"Pratyusha Sharma, Antonio Torralba, Jacob Andreas","year":"2022","venue":"Proceedings of the Annual Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Shin et al., 2021] Richard Shin, Christopher H. Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Anto- nios Platanios, Adam Pauls, Dan Klein, Jason Eisner, and Benjamin Van Durme. Constrained language models yield few-shot semantic parsers. In Marie-Francine Moens, Xu- anjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pages 7699-7715. Association for Computational Linguistics, 2021.","title":"Constrained language models yield few-shot semantic parsers","authors":"Richard Shin, Christopher H Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme","year":"2021","venue":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Shridhar et al., 2020] Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mot- taghi, Luke Zettlemoyer, and Dieter Fox. Alfred: A bench- mark for interpreting grounded instructions for everyday tasks. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, pages 10740-10749, 2020.","title":"Alfred: A benchmark for interpreting grounded instructions for everyday tasks","authors":"Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, Dieter Fox","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Singh et al., 2022] Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Prog- prompt: Generating situated robot task plans using large language models. arXiv: 2209.11302, 2022.","title":"Progprompt: Generating situated robot task plans using large language models","authors":"Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, Animesh Garg","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Suzgun et al., 2022] Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022.","title":"Challenging big-bench tasks and whether chain-of-thought can solve them","authors":"Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, V Quoc, Ed H Le, Denny Chi, Zhou","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Thoppilan et al., 2022] Romal Thoppilan, Daniel De Fre- itas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos San- tos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinod- kumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Agüera y Ar- cas, Claire Cui, Marian Croak, Ed H. Chi, and Quoc Le. Lamda: Language models for dialog applications. CoRR, abs/2201.08239, 2022.","title":"Lamda: Language models for dialog applications","authors":"Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze, Alicia Cheng, Taylor Jin, Leslie Bos, Yu Baker, Yaguang Du, Hongrae Li, Huaixiu Lee, Amin Steven Zheng, Marcelo Ghafouri, Yanping Menegali, Maxim Huang, Dmitry Krikun, James Lepikhin, Dehao Qin, Yuanzhong Chen, Zhifeng Xu, Adam Chen, Maarten Roberts, Yanqi Bosma, Chung-Ching Zhou, Igor Chang, Will Krivokon, Marc Rusch, Kathleen S Pickett, Meredith Ringel Meier-Hellstern, Tulsee Morris, Renelito Delos Doshi, Toju Santos, Johnny Duke, Ben Soraker, Vinodkumar Zevenbergen, Mark Prabhakaran, Ben Diaz, Kristen Hutchinson, Alejandra Olson, Erin Molina, Josh Hoffman-John, Lora Lee, Ravi Aroyo, Alena Rajakumar, Matthew Butryna, Viktoriya Lamm, Joe Kuzmina, Aaron Fenton, Rachel Cohen, Ray Bernstein, Blaise Kurzweil, Claire Agüera Y Arcas, Marian Cui, Ed H Croak, Quoc Chi, Le","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Touvron et al., 2023] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine- tuned chat models. arXiv preprint arXiv:2307.09288, 2023.","title":"Llama 2: Open foundation and finetuned chat models","authors":"Touvron","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wei et al., 2022] Jason Wei, Xuezhi Wang, Dale Schuur- mans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824-24837, 2022.","title":"Chain-of-thought prompting elicits reasoning in large language models","authors":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, V Quoc, Denny Le, Zhou","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wolfson et al., 2020] Tomer Wolfson, Mor Geva, Ankit Gupta, Yoav Goldberg, Matt Gardner, Daniel Deutch, and Jonathan Berant. Break it down: A question understanding benchmark. Trans. Assoc. Comput. Linguistics, 8:183-198, 2020.","title":"Break it down: A question understanding benchmark","authors":"Tomer Wolfson, Mor Geva, Ankit Gupta, Yoav Goldberg, Matt Gardner, Daniel Deutch, Jonathan Berant","year":"2020","venue":"Trans. Assoc. Comput. Linguistics","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang et al., 2018] Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600, 2018.","title":"Hotpotqa: A dataset for diverse, explainable multi-hop question answering","authors":"Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, Christopher D Manning","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yao et al., 2023] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language mod- els. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.","title":"React: Synergizing reasoning and acting in language models","authors":"Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, R Karthik, Yuan Narasimhan, Cao","year":"2023","venue":"The Eleventh International Conference on Learning Representations, ICLR 2023","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}