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We propose Heterogeneous Swarms, an algorithm to design multi-LLM systems by jointly optimizing model roles and weights.
Particle swarm optimization
James Kennedy and Russell Eberhart · 1995
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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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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
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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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A dataset of information-seeking questions and answers anchored in research papers
Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A Smith, and Matt Gardner · 2021
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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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Branch-train-merge: Embarrassingly parallel training of expert language models
Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A Smith, and Luke Zettlemoyer · 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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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin · 2023
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Camels in a changing climate: Enhancing lm adaptation with tulu 2
Hamish Ivison, Yizhong Wang, Valentina Pyatkin, Nathan Lambert, Matthew Peters, Pradeep Dasigi, Joel Jang, David Wadden, Noah A Smith, Iz Beltagy, et al · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Language generation models can cause harm: So what can we do about it? an actionable survey
Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, and Yulia Tsvetkov · 2023
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Encouraging divergent thinking in large language models through multi-agent debate
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu · 2023
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Getting more out of mixture of language model reasoning experts
Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, and Jordan Boyd-Graber · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al · 2023
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Malicious agent detection for robust multi-agent collaborative perception
Yangheng Zhao, Zhen Xiang, Sheng Yin, Xianghe Pang, Siheng Chen, and Yanfeng Wang · 2023
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Evolutionary optimization of model merging recipes
Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, and David Ha · 2024
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Agentharm: A benchmark for measuring harmfulness of llm agents
Maksym Andriushchenko, Alexandra Souly, Mateusz Dziemian, Derek Duenas, Maxwell Lin, Justin Wang, Dan Hendrycks, Andy Zou, Zico Kolter, Matt Fredrikson, et al · 2024
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al · 2024
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Reflective multi-agent collaboration based on large language models
Xiaohe Bo, Zeyu Zhang, Quanyu Dai, Xueyang Feng, Lei Wang, Rui Li, Xu Chen, and Ji-Rong Wen · 2024
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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 2024
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Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al · 2024
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Agentboard: An analytical evaluation board of multi-turn llm agents
Ma Chang, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, and Junxian He · 2024
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Embodied cot distillation from llm to off-the-shelf agents
Wonje Choi, Woo Kyung Kim, Minjong Yoo, and Honguk Woo · 2024
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Agent instructs large language models to be general zero-shot reasoners
Nicholas Crispino, Kyle Montgomery, Fankun Zeng, Dawn Song, and Chenguang Wang · 2024
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Hypothetical minds: Scaffolding theory of mind for multi-agent tasks with large language models
Logan Cross, Violet Xiang, Agam Bhatia, Daniel LK Yamins, and Nick Haber · 2024
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Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents
Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr · 2024
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Knowledge crosswords: Geometric knowledge reasoning with large language models
Wenxuan Ding, Shangbin Feng, Yuhan Liu, Zhaoxuan Tan, Vidhisha Balachandran, Tianxing He, and Yulia Tsvetkov · 2024
Cited alongside, same era.
Goal: A generalist combinatorial optimization agent learner
Darko Drakulic, Sofia Michel, and Jean-Marc Andreoli · 2024
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Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch · 2024
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Complex reasoning over logical queries on commonsense knowledge graphs
Tianqing Fang, Zeming Chen, Yangqiu Song, and Antoine Bosselut · 2024
Cited alongside, same era.
Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2024
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Agentsquare: Automatic llm agent search in modular design space
Yu Shang, Yu Li, Keyu Zhao, Likai Ma, Jiahe Liu, Fengli Xu, and Yong Li · 2024
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Learning to decode collaboratively with multiple language models
Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, and David Sontag · 2024
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Should we be going mad? a look at multi-agent debate strategies for llms
Andries Petrus Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D Barrett, and Arnu Pretorius · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Promptbreeder: Self-referential self-improvement via prompt evolution
Chrisantha Fernando, Dylan Sunil Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel · 2024
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Gemma: Open models based on gemini research and technology
Gemma Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
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Nexus: Specialization meets adaptability for efficiently training mixture of experts
Nikolas Gritsch, Qizhen Zhang, Acyr Locatelli, Sara Hooker, and Ahmet Üstün · 2024
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Richelieu: Self-evolving llm-based agents for ai diplomacy
Zhenyu Guan, Xiangyu Kong, Fangwei Zhong, and Yizhou Wang · 2024
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Automated design of agentic systems
Shengran Hu, Cong Lu, and Jeff Clune · 2024
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Model stock: All we need is just a few fine-tuned models
Dong-Hwan Jang, Sangdoo Yun, and Dongyoon Han · 2024
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Online relational inference for evolving multi-agent interacting systems
Beomseok Kang, Priyabrata Saha, Sudarshan Sharma, Biswadeep Chakraborty, and Saibal Mukhopadhyay · 2024
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To cot or not to cot? chain-of-thought helps mainly on math and symbolic reasoning
Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, and Greg Durrett · 2024
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Magis: Llm-based multi-agent framework for github issue resolution
Wei Tao, Yucheng Zhou, Yanlin Wang, Wenqiang Zhang, Hongyu Zhang, and Yu Cheng · 2024
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Teach better or show smarter? on instructions and exemplars in automatic prompt optimization
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, and Sercan O Arik · 2024
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Reinforcing llm agents via policy optimization with action decomposition
Muning Wen, Ziyu Wan, Jun Wang, Weinan Zhang, and Ying Wen · 2024
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Autogen: Enabling next-gen llm applications via multi-agent conversation
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al · 2024
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Language agents with reinforcement learning for strategic play in the werewolf game
Zelai Xu, Chao Yu, Fei Fang, Yu Wang, and Yi Wu · 2024
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Watch out for your agents! investigating backdoor threats to llm-based agents
Wenkai Yang, Xiaohan Bi, Yankai Lin, Sishuo Chen, Jie Zhou, and Xu Sun · 2024
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li · 2024
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Competeai: Understanding the competition dynamics of large language model-based agents
Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, and Xing Xie · 2024
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Star-agents: Automatic data optimization with llm agents for instruction tuning
Hang Zhou, Yehui Tang, Haochen Qin, Yujie Yang, Renren Jin, Deyi Xiong, Kai Han, and Yunhe Wang · 2024
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GPTSwarm: Language agents as optimizable graphs
Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber · 2024
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Collab: Controlled decoding using mixture of agents for llm alignment
Souradip Chakraborty, Sujay Bhatt, Udari Madhushani Sehwag, Soumya Suvra Ghosal, Jiahao Qiu, Mengdi Wang, Dinesh Manocha, Furong Huang, Alec Koppel, and Sumitra Ganesh · 2025
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Single-agent poisoning attacks suffice to ruin multi-agent learning
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