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Solving complex real-world tasks requires cycles of actions and observations.
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Advances and applications of molecular cloning in clinical microbiology
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MAESTRO–multi agent stability prediction upon point mutations
Josef Laimer, Heidi Hofer, Marko Fritz, Stefan Wegenkittl, and Peter Lackner · 2015
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Artificial intelligence: a modern approach
Stuart J Russell and Peter Norvig · 2016
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Thinking fast and slow with deep learning and tree search
Thomas Anthony, Zheng Tian, and David Barber · 2017
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Methods of Cloning
Alessandro Bertero, Stephanie Brown, and Ludovic Vallier · 2017
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MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 2017
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
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Role of biocatalysis in sustainable chemistry
Roger A Sheldon and John M Woodley · 2018
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Principles of protein stability and their application in computational design
Adi Goldenzweig and Sarel J Fleishman · 2018
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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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Deep learning in protein structural modeling and design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray · 2020
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AutoPrompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Computational modeling of protein stability: Quantitative analysis reveals solutions to pervasive problems
Aron Broom, Kyle Trainor, Zachary Jacobi, and Elizabeth M. Meiering · 2020
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Prediction of protein mutational free energy: Benchmark and sampling improvements increase classification accuracy
Brandon Frenz, Steven M Lewis, Indigo King, Frank DiMaio, Hahnbeom Park, and Yifan Song · 2020
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Expert iteration
Thomas William Anthony · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner · 2021
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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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Collaborating with language models for embodied reasoning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, and Rob Fergus · 2022
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Solving math word problems with process-and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
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LangChain, October 2022
Harrison Chase · 2022
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LlamaIndex, November 2022
Jerry Liu · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction
Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen · 2022
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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
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ScienceWorld: Is your agent smarter than a 5th grader?
Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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High-resolution de novo structure prediction from primary sequence
Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, Jianzhu Ma, and Jian Peng · 2022
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Augmented language models: a survey
Grégoire Mialon, Roberto Dessi, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Roziere, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al · 2023
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The rise and potential of large language model based agents: A survey
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al · 2023
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Large language models empowered agent-based modeling and simulation: A survey and perspectives
Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, and Yong Li · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Eight things to know about large language models
Samuel R Bowman · 2023
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Socratic models: Composing zero-shot multimodal reasoning with language
Andy Zeng, Maria Attarian, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S Ryoo, Vikas Sindhwani, Johnny Lee, et al · 2023
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Selection-inference: exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins · 2023
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Do as I can, not as I say: grounding language in robotic affordances
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al · 2023
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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 · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Wang, and Zhiting Hu · 2023
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein · 2023
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PaperQA: Retrieval-augmented generative agent for scientific research
Jakub Lála, Odhran O’Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G Rodriques, and Andrew D White · 2023
Cited alongside, same era.
A new age in protein design empowered by deep learning
Hamed Khakzad, Ilia Igashov, Arne Schneuing, Casper Goverde, Michael Bronstein, and Bruno Correia · 2023
Cited alongside, same era.
On the Design and Analysis of LLM-Based Algorithms
Yanxi Chen, Yaliang Li, Bolin Ding, and Jingren Zhou · 2024
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Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang · 2024
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Are large language models good prompt optimizers?
Ruotian Ma, Xiaolei Wang, Xin Zhou, Jian Li, Nan Du, Tao Gui, Qi Zhang, and Xuanjing Huang · 2024
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Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers
Tuo Zhang, Jinyue Yuan, and Salman Avestimehr · 2024
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Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen · 2024
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LLM-powered autonomous agents
Lilian Weng · 2023
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Grounding large language models in interactive environments with online reinforcement learning
Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer · 2023
Cited alongside, same era.
Pangu-Agent: A fine-tunable generalist agent with structured reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer, Thomas Coste, Zhihao Wu, Jingxuan Chen, Khyati Khandelwal, James Doran, Xidong Feng, Jiacheng Liu, et al · 2023
Cited alongside, same era.
Cost-effective hyperparameter optimization for large language model generation inference
Chi Wang, Xueqing Liu, and Ahmed Hassan Awadallah · 2023
Cited alongside, same era.
Black-box prompt optimization: Aligning large language models without model training
Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, and Minlie Huang · 2023
Cited alongside, same era.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2023
Cited alongside, same era.
Automatic prompt optimization with ”gradient descent” and beam search
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng · 2023
Cited alongside, same era.
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Prompt optimization with human feedback
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Localized zeroth-order prompt optimization
Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin, Zhongxiang Dai, See-Kiong Ng, and Bryan Kian Hsiang Low · 2024
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Prompt optimization with EASE? Efficient ordering-aware automated selection of exemplars
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Prompt optimization in large language models
Antonio Sabbatella, Andrea Ponti, Ilaria Giordani, Antonio Candelieri, and Francesco Archetti · 2024
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PromptAgent: Strategic planning with language models enables expert-level prompt optimization
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Improving text-to-image consistency via automatic prompt optimization
Oscar Mañas, Pietro Astolfi, Melissa Hall, Candace Ross, Jack Urbanek, Adina Williams, Aishwarya Agrawal, Adriana Romero-Soriano, and Michal Drozdzal · 2024
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Joint prompt optimization of stacked LLMs using variational inference
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Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
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AvaTaR: Optimizing LLM agents for tool-assisted knowledge retrieval
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Tool learning with large language models: A survey
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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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Agent Lumos: Unified and modular training for open-source language agents
Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, and Bill Yuchen Lin · 2024
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Symbolic learning enables self-evolving agents
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Automated design of agentic systems
Shengran Hu, Cong Lu, and Jeff Clune · 2024
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DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, Heather Miller, et al · 2024
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OpenR: An open source framework for advanced reasoning with large language models
Jun Wang, Meng Fang, Ziyu Wan, Muning Wen, Jiachen Zhu, Anjie Liu, Ziqin Gong, Yan Song, Lei Chen, Lionel M Ni, et al · 2024
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MLAgentBench: Evaluating language agents on machine learning experimentation
Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec · 2024
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DS-Agent: Automated data science by empowering large language models with case-based reasoning
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Large language models orchestrating structured reasoning achieve Kaggle grandmaster level
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InfiAgent-DABench: Evaluating agents on data analysis tasks
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Tapilot-Crossing: Benchmarking and evolving llms towards interactive data analysis agents
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SciAgent: Tool-augmented language models for scientific reasoning
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Large language monkeys: Scaling inference compute with repeated sampling
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LLMs are highly-constrained biophysical sequence optimizers
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Anthropic · 2024
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The Llama 3 herd of models, 2024
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