Fetching the paper…
Reading the bibliography…
Since their inception, programming languages have trended towards greater readability and lower barriers for programmers.
Structured programming
Ole-Johan Dahl, Edsger Wybe Dijkstra, and Charles Antony Richard Hoare. 1972 · 1972
Earlier work this paper cites.
Automatic programming through natural language dialogue: A survey
George E Heidorn. 1976 · 1976
Earlier work this paper cites.
Regular expressions for program computations
Ronald E Prather. 1997 · 1997
Earlier work this paper cites.
Should we leverage natural-language knowledge? An analysis of user errors in a natural-language-style programming language. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems . 207–214
Amy Bruckman and Elizabeth Edwards. 1999 · 1999
Earlier work this paper cites.
Programming with unrestricted natural language. In Proceedings of the Australasian Language Technology Workshop 2005 . 191–199
David Vadas and James R Curran. 2005 · 2005
Earlier work this paper cites.
NLP (natural language processing) for NLP (natural language programming). In Computational Linguistics and Intelligent Text Processing: 7th International Conference, CICLing 2006, Mexico City, Mexico, February 19-25, 2006. Proceedings 7 . Springer, 319–330
Rada Mihalcea, Hugo Liu, and Henry Lieberman. 2006 · 2006
Earlier work this paper cites.
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda. 2020 · 2006
Earlier work this paper cites.
Computer architecture: a quantitative approach
John L Hennessy and David A Patterson. 2011 · 2011
Earlier work this paper cites.
The NLP engine: A universal turing machine for nlp
Jiwei Li and Eduard Hovy. 2015 · 2015
Earlier work this paper cites.
Program synthesis using natural language. In Proceedings of the 38th International Conference on Software Engineering . 345–356
Aditya Desai, Sumit Gulwani, Vineet Hingorani, Nidhi Jain, Amey Karkare, Mark Marron, and Subhajit Roy. 2016 · 2016
Earlier work this paper cites.
Natural language is a programming language: Applying natural language processing to software development. In 2nd Summit on Advances in Programming Languages (SNAPL 2017) . Schloss-Dagstuhl-Leibniz Zentrum für Informatik
Michael D Ernst. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Earlier work this paper cites.
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Improving language models by retrieving from trillions of tokens. In International conference on machine learning . PMLR, 2206–2240
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
Earlier work this paper cites.
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
Earlier work this paper cites.
Demonstrate-Search-Predict: Composing Retrieval and Language Models for Knowledge-Intensive NLP
Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. 2022 · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Cited alongside, same era.
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2022 · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Later among the works it cites.
Josh et al OpenAI. 2023 · 2023
Later among the works it cites.
Refiner: Reasoning feedback on intermediate representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings. 2023 · 2023
Later among the works it cites.
Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
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
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. 2022 · 2022
Cited alongside, same era.
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023b · 2023
Cited alongside, same era.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023a · 2023
Cited alongside, same era.
Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
Cited alongside, same era.
OpenAGI: When LLM Meets Domain Experts
Yingqiang Ge, Wenyue Hua, Kai Mei, Jianchao Ji, Juntao Tan, Shuyuan Xu, Zelong Li, and Yongfeng Zhang. 2023a · 2023
Cited alongside, same era.
LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem
Yingqiang Ge, Yujie Ren, Wenyue Hua, Shuyuan Xu, Juntao Tan, and Yongfeng Zhang. 2023b · 2023
Cited alongside, same era.
The programmer’s assistant: Conversational interaction with a large language model for software development. In Proceedings of the 28th International Conference on Intelligent User Interfaces . 491–514
Steven I Ross, Fernando Martinez, Stephanie Houde, Michael Muller, and Justin D Weisz. 2023 · 2023
Later among the works it cites.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023 · 2023
Later among the works it cites.
Progprompt: Generating situated robot task plans using large language models. In 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 11523–11530
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. 2023 · 2023
Later among the works it cites.
Augmenting black-box llms with medical textbooks for clinical question answering
Yubo Wang, Xueguang Ma, and Wenhu Chen. 2023 · 2023
Later among the works it cites.
ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR)
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023 · 2023
Later among the works it cites.
Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 17682–17690
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al · 2024
Closest in time.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2024
Closest in time.
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
Closest in time.
Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents
Zelong Li, Wenyue Hua, Hao Wang, He Zhu, and Yongfeng Zhang. 2024 · 2024
Closest in time.
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 · 2024
Closest in time.
AIOS: LLM Agent Operating System
Kai Mei, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, and Yongfeng Zhang. 2024 · 2024
Closest in time.
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows
Yiran Wu, Tianwei Yue, Shaokun Zhang, Chi Wang, and Qingyun Wu. 2024 · 2024
Closest in time.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
Closest in time.
Task and motion planning with large language models for object rearrangement. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2086–2092
Yan Ding, Xiaohan Zhang, Chris Paxton, and Shiqi Zhang. 2023 · 2092
Closest in time.