From cf93186306571e58a04d2af9eaa51a1bd4f7747c Mon Sep 17 00:00:00 2001 From: Maxime MORGE <maxime.morge@univ-lille.fr> Date: Mon, 27 Jan 2025 14:06:52 +0100 Subject: [PATCH] LLM4AAMAS: initial version --- README.md | 241 ++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 233 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 5b3bf64..b4fd74c 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,243 @@ # LLM4AAMAS -Les Systèmes Multi-agents et Agents Autonomes (SMAA) génératifs ouvrent des -perspectives prometteuses pour résoudre des problèmes dans des environnements -ouverts et simuler des dynamiques sociales complexes. +Generative Autonomous Agents and Multi-Agent Systems (AAMAS) offer promising +opportunities for solving problems in open environments and simulating complex +social dynamics. +This repository contains a collection of papers and ressources related +to generative AAMAS. This list is a work in progress and will be regularly updated with new resources. -L'objectif de ce dépôt est de constituer une collection de ressources -pertinentes pour les SMAA génératifs que nous nous efforçons de mettre à jour -régulièrement et en continu. -## Auteurs +## Artificial Intelligence + +- **[Intelligence artificielle : une approche moderne (4e édition)](https://hal.archives-ouvertes.fr/hal-04245057)** + *Stuart Russell, Peter Norvig, Fabrice Popineau, Laurent Miclet, Claire Cadet (2021)* + Publisher: Pearson France + +- **[Apprentissage artificiel - 3e édition : Deep learning, concepts et algorithmes](https://www.eyrolles.com/)** + *Antoine Cornuéjols, Laurent Miclet, Vincent Barra (2018)* + Publisher: Eyrolles + + +## Neural networks (RNN, Transformers) + +- **[Learning representations by back-propagating errors](https://doi.org/10.1038/323533a0)** + *David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams (1986)* + Published in *Nature* + +- **[ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)** + *Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (2012)* + Presented at *NeurIPS* + + +## Large Language Models + +- **[A Survey of Large Language Models](https://arxiv.org/abs/2303.18223)** + *Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. (2024)* + Published on *arXiv* + +- **[Large Language Model based Multi-Agents: A Survey of Progress and + Challenges](https://arxiv.org/abs/2402.01680)** *Taicheng Guo et al. (2024)* + Published on *arXiv* arXiv:2402.01680 [cs.CL] + +- **[Improving language understanding by generative + pre-training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)** + *Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever (2018)* + Published by OpenAI + +- **[BERT: Pre-training of Deep Bidirectional Transformers for Language + Understanding](https://www.aclweb.org/anthology/N19-1423/)** *Jacob Devlin, + Ming-Wei Chang, Kenton Lee, Kristina Toutanova (2019)* Presented at + *NAACL-HLT* + +- **[Sequence to Sequence Learning with Neural + Networks](https://arxiv.org/abs/1409.3215)** *Ilya Sutskever, Oriol Vinyals, + Quoc V. Le (2014)* Published on *arXiv* + +- **[Learning Phrase Representations using RNN Encoder-Decoder for Statistical + Machine Translation](https://arxiv.org/abs/1406.1078)** *Kyunghyun Cho, Bart + van Merrienboer, Caglar Gulcehre, et al. (2014)* Published on *arXiv* + +## Tuning + +### Instruction tuning + +- **[LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)** + *Edward J. Hu, Yelong Shen, Phillip Wallis, et al. (2021)* + Published on *arXiv* + +- **[Language Models are Few-Shot + Learners](https://papers.nips.cc/paper/2020/file/fc2c7f9a3f3f86cde5d8ad2c7f7e57b2-Paper.pdf)** + *Tom Brown, Benjamin Mann, Nick Ryder, et al. (2020)* Presented at *NeurIPS* + +### Alignement tuning + +- **[Training language models to follow instructions with human + feedback](https://papers.nips.cc/paper/2022/hash/17f4c5f98073d1fb95f7e53f5c7fdb64-Abstract.html)** + *Long Ouyang, Jeffrey Wu, Xu Jiang, et al. (2022)* Presented at *NeurIPS* + +## Existing LLMs + +Many models are available at the following URLs: +[https://www.nomic.ai/gpt4all](https://www.nomic.ai/gpt4all) and +[https://huggingface.co/models](https://huggingface.co/models). + +- **[GPT-4 Technical Report](https://arxiv.org/abs/2303.08774)** + *OpenAI Team (2024)* + Published on *arXiv* + +- **[The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783)** + *Meta Team (2024)* + Published on *arXiv* + +- **[Stanford Alpaca: An Instruction-Following LLaMa Model](https://github.com/tatsu-lab/stanford_alpaca)** + *Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, et al. (2023)* + Published on *GitHub* + +- **[Mixtral of Experts](https://arxiv.org/abs/2401.04088)** + *Mistral AI team (2024)* + Published on *arXiv* + +- **[Mistral 7B](https://arxiv.org/abs/2310.06825)** + *Mistral AI team (2023)* + Published on *arXiv* + +- **[The Lucie-7B LLM and the Lucie Training Dataset: Open Resources for + Multilingual Language Generation](https://arxiv.org/abs/)** *Olivier Gouvert, + Julie Hunter, Jérôme Louradour, Evan Dufraisse, Yaya Sy, Pierre-Carl Langlais, + Anastasia Stasenko, Laura Rivière, Christophe Cerisara, Jean-Pierre Lorré + (2025)* + + +## Prompt engineering + +### ICL + +- **A Survey on In-context Learning** *Qingxiu Dong, Lei Li, Damai Dai, Ce + Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, + Xu Sun, Lei Li, Zhifang Sui (2024)* Presented at the *Conference on Empirical + Methods in Natural Language Processing (EMNLP)* Location: Miami, Florida, USA + Published by: Association for Computational Linguistics + +### CoT + +- **[Chain-of-Thought Prompting Elicits Reasoning in Large Language + Models](https://papers.nips.cc/paper/52604-chain-of-thought-prompting-elicits-reasoning-in-large-language-models)** + *Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, et al. (2022)* + Presented at *NeurIPS* + + +### RAG + +- **[Retrieval-Augmented Generation for Large Language Models: A + Survey](https://arxiv.org/abs/2312.10997)** *Yunfan Gao, Yun Xiong, Xinyu Gao, + Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang + (2024)* Published on *arXiv* + +## Generative Autonomous Agents + +- **A Survey on Large Language Model Based Autonomous Agents** Lei Wang, Chen + Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai + Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Jirong Wen (2024)* + Published in *Frontiers of Computer Science* (Volume 18, Issue 6, Pages + 186345) Publisher: Springer + + +- **[HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging + Face](https://papers.nips.cc/paper/2023/hash/38154-hugginggpt-solving-ai-tasks-with-chatgpt-and-its-friends-in-hugging-face.pdf)** + *Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang + (2023)* Presented at *Advances in Neural Information Processing Systems + (NeurIPS)* Pages: 38154–38180 Publisher: Curran Associates, Inc. Volume: 36 + +- **[Toolformer: Language Models Can Teach Themselves to Use Tools](https://papers.nips.cc/paper/86759-toolformer-language-models-can-teach-themselves-to-use-tools)** + *Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, et al. (2023)* + Presented at *NeurIPS* + +- **[Cognitive Architectures for Language Agents](https://arxiv.org/abs/2309.02427)** + *Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths (2024)* + Published on *arXiv* + + +### Generative Autonomous Agents on the shelf + +- [LangChain](https://www.langchain.com) is an open-source framework for + designing prompts for LLMs. It can be used to define high-level reasoning + sequences, conversational agents, RAGs (Retrieval-Augmented Generation), + document summaries, or even the generation of synthetic data. + +- [LangGraph](https://langchain-ai.github.io/langgraph) is a low-level library + for the design of cognitive architecture for autonomous agents, whose + reasoning engine is an LLM. + +- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) is a platform for + the creation, deployment, and management of generative agents. + +- [WorkGPT](https://github.com/team-openpm/workgpt) is similar to AutoGPT + + +## Generative MAS + +- **[Large language models empowered agent-based modeling and simulation: A + survey and perspectives](https://doi.org/10.1057/s41599-024-01235-9)** **Chen + Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, + Yong Li (2024)* Published in *Humanities and Social Sciences Communications*, + Volume 11, Issue 1, Pages 1–24 + +- **[Social Simulacra: Creating Populated Prototypes for Social Computing + Systems](https://dl.acm.org/doi/10.1145/3526110.3545617)** *Joon Sung Park, + Lindsay Popowski, Carrie Cai, Meredith Ringel Morris, Percy Liang, Michael S. + Bernstein (2022)* Published in *Proceedings of the 35th Annual ACM Symposium + on User Interface Software and Technology* Articleno: 74, Pages: 18, Location: + Bend, OR, USA + +- **[Generative Agents: Interactive Simulacra of Human + Behavior](https://dl.acm.org/doi/10.1145/3586184.3594067)** *Joon Sung Park, + Joseph O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, Michael + S. Bernstein (2023)* Published in *Proceedings of the 36th Annual ACM + Symposium on User Interface Software and Technology* Articleno: 2, Pages: 22, + Location: San Francisco, CA, USA, Series: UIST '23 + +- **[Agentverse: Facilitating multi-agent collaboration and exploring emergent + behaviors](https://openreview.net/forum?id=HywBMyh6JGR)** *Weize Chen, Yusheng + Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, + Yi-Hsin Hung, Chen Qian, et al. (2023)* Published in *The Twelfth + International Conference on Learning Representations (ICLR 2023)* + +- **[Training socially aligned language models on simulated social + interactions](https://arxiv.org/abs/2305.16960)** *Ruibo Liu, Ruixin Yang, + Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M. Dai, Diyi Yang, Soroush Vosoughi + (2023)* Published on *arXiv* arXiv:2305.16960 + +- [S3: Social-network Simulation System with Large Language Model-Empowered + Agents](https://arxiv.org/abs/2307.14984)** *Chen Gao, Xiaochong Lan, Zhihong + Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, Yong Li (2023)* + Published on *arXiv* arXiv:2307.14984 + +### Generative MAS on the shelf + +- [MetaGPT](https://github.com/geekan/MetaGPT) is a framework for creating + generative MAS dedicated to software development. + +- [CAMEL](https://github.com/camel-ai/camel) proposes a generative multi-agent + framework for accomplishing complex tasks. + +- [AutoGen](https://github.com/microsoft/autogen) is a versatile open-source + framework for creating generative multi-agent systems. + +## Authors Maxime MORGE ## License -TBA +This program is free software: you can redistribute it and/or modify it under +the terms of the GNU General Public License as published by the Free Software +Foundation, either version 3 of the License, or (at your option) any later +version. + +This program is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +PARTICULAR PURPOSE. See the GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along with +this program. If not, see <http://www.gnu.org/licenses/>. -- GitLab