To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning

ν \nu z-an optimizing smt solver

Nikolaj Bjørner, Anh-Dung Phan, and Lars Fleckenstein. 2015 · 2015

Earlier work this paper cites.

Artificial intelligence: a modern approach

Stuart J Russell and Peter Norvig. 2016 · 2016

Earlier work this paper cites.

Good and bad market research: A critical review of net promoter score

Nicholas I Fisher and Raymond E Kordupleski. 2019 · 2019

Earlier work this paper cites.

The scip optimization suite 8.0

Original

Ksenia Bestuzheva, Mathieu Besançon, Wei-Kun Chen, Antonia Chmiela, Tim Donkiewicz, Jasper van Doornmalen, Leon Eifler, Oliver Gaul, Gerald Gamrath, Ambros Gleixner, et al. 2021 · 2021

Earlier work this paper cites.

Survey of hallucination in natural language generation

Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Delong Chen, Wenliang Dai, Andrea Madotto, and Pascale Fung. 2022 · 2022

Earlier work this paper cites.

Pddl planning with pretrained large language models

Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. 2022 · 2022

Earlier work this paper cites.

Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)

Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 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, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Earlier work this paper cites.

React: Synergizing reasoning and acting in language models

Original

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022

Earlier work this paper cites.

Grammar-constrained decoding for structured nlp tasks without finetuning

Original

Saibo Geng, Martin Josifoski, Maxime Peyrard, and Robert West. 2023 · 2023

Earlier work this paper cites.

Metagpt: Meta programming for multi-agent collaborative framework

Original

Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al. 2023 · 2023

Cited alongside, same era.

Efficient memory management for large language model serving with pagedattention

Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023

Cited alongside, same era.

Llm+ p: Empowering large language models with optimal planning proficiency

Original

Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. 2023 · 2023

Cited alongside, same era.

Autogen: Enabling next-gen llm applications via multi-agent conversation framework

Original

Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023 · 2023

Cited alongside, same era.

Reflexion: Language agents with verbal reinforcement learning

Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024

Closest in time.

Generalized planning in pddl domains with pretrained large language models

Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B Tenenbaum, Leslie Kaelbling, and Michael Katz. 2024 · 2024

Closest in time.

Llms can’t be trusted for financial advice

Gary Smith. 2024 · 2024

Closest in time.

Synergizing spatial optimization with large language models for open-domain urban itinerary planning

Original

Yihong Tang, Zhaokai Wang, Ao Qu, Yihao Yan, Kebing Hou, Dingyi Zhuang, Xiaotong Guo, Jinhua Zhao, Zhan Zhao, and Wei Ma. 2024 · 2024

Closest in time.

Easy problems that llms get wrong

Original

Sean Williams and James Huckle. 2024 · 2024

Closest in time.