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Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions through proper grounding.
Introspective Learning and Reasoning
David B. Leake · 2012
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Analyzing uncertainty in neural machine translation
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Least ambiguous set-valued classifiers with bounded error levels
Mauricio Sadinle, Jing Lei, and Larry Wasserman · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Calibration of pre-trained transformers
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Unsupervised quality estimation for neural machine translation
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
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How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al · 2022
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Anastasios N Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei, and Tal Schuster · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
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Inner monologue: Embodied reasoning through planning with language models
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
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Refiner: Reasoning feedback on intermediate representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings · 2023
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In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2023
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Robots that ask for help: Uncertainty alignment for large language model planners
Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, and Anirudha Majumdar · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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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
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Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2022
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2022
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Uncertainty in natural language generation: From theory to applications
Joris Baan, Nico Daheim, Evgenia Ilia, Dennis Ulmer, Haau-Sing Li, Raquel Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, and Wilker Aziz · 2023
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Prompting large language models with the socratic method
Edward Y Chang · 2023
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Noah Shinn, Beck Labash, and Ashwin Gopinath · 2023
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Progprompt: Generating situated robot task plans using large language models
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg · 2023
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Task ambiguity in humans and language models
Alex Tamkin, Kunal Handa, Avash Shrestha, and Noah Goodman · 2023
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Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang · 2023
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Recomp: Improving retrieval-augmented lms with compression and selective augmentation
Fangyuan Xu, Weijia Shi, and Eunsol Choi · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Do large language models know what they don’t know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang · 2023
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Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant · 2023
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Demonstrating Large Language Models on Robots
Andy Zeng, Brian Ichter, Fei Xia, Ted Xiao, and Vikas Sindhwani · 2023
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2023
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Language agent tree search unifies reasoning acting and planning in language models
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang · 2023
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Llms can’t plan, but can help planning in llm-modulo frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Kaya Stechly, Mudit Verma, Siddhant Bhambri, Lucas Saldyt, and Anil Murthy · 2024
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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
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