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<h1 id="llm4aamas">LLM4AAMAS</h1>
<p>Generative Autonomous Agents and Multi-Agent Systems (AAMAS) offer
promising opportunities for solving problems in open environments and
simulating complex social dynamics.</p>
<p>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.</p>
<h2 id="artificial-intelligence">Artificial Intelligence</h2>
<ul>
<li><p><strong><a
href="https://hal.archives-ouvertes.fr/hal-04245057">Intelligence
artificielle : une approche moderne (4e édition)</a></strong> <em>Stuart
Russell, Peter Norvig, Fabrice Popineau, Laurent Miclet, Claire Cadet
(2021)</em> Publisher: Pearson France</p></li>
<li><p><strong><a href="https://www.eyrolles.com/">Apprentissage
artificiel - 3e édition : Deep learning, concepts et
algorithmes</a></strong> <em>Antoine Cornuéjols, Laurent Miclet, Vincent
Barra (2018)</em> Publisher: Eyrolles</p></li>
</ul>
<h2 id="neural-networks-rnn-transformers">Neural networks (RNN,
Transformers)</h2>
<ul>
<li><p><strong><a href="https://doi.org/10.1038/323533a0">Learning
representations by back-propagating errors</a></strong> <em>David E.
Rumelhart, Geoffrey E. Hinton, Ronald J. Williams (1986)</em> Published
in <em>Nature</em></p></li>
<li><p><strong><a
href="https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks">ImageNet
Classification with Deep Convolutional Neural Networks</a></strong>
<em>Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (2012)</em>
Presented at <em>NeurIPS</em></p></li>
</ul>
<h2 id="large-language-models">Large Language Models</h2>
<ul>
<li><p><strong><a href="https://arxiv.org/abs/2303.18223">A Survey of
Large Language Models</a></strong> <em>Wayne Xin Zhao, Kun Zhou, Junyi
Li, et al. (2024)</em> Published on <em>arXiv</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/2402.01680">Large Language
Model based Multi-Agents: A Survey of Progress and
Challenges</a></strong> <em>Taicheng Guo et al. (2024)</em> Published on
<em>arXiv</em> arXiv:2402.01680 [cs.CL]</p></li>
<li><p><strong><a
href="https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf">Improving
language understanding by generative pre-training</a></strong> <em>Alec
Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever (2018)</em>
Published by OpenAI</p></li>
<li><p><strong><a
href="https://www.aclweb.org/anthology/N19-1423/">BERT: Pre-training of
Deep Bidirectional Transformers for Language Understanding</a></strong>
<em>Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
(2019)</em> Presented at <em>NAACL-HLT</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/1409.3215">Sequence to
Sequence Learning with Neural Networks</a></strong> <em>Ilya Sutskever,
Oriol Vinyals, Quoc V. Le (2014)</em> Published on
<em>arXiv</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/1406.1078">Learning Phrase
Representations using RNN Encoder-Decoder for Statistical Machine
Translation</a></strong> <em>Kyunghyun Cho, Bart van Merrienboer, Caglar
Gulcehre, et al. (2014)</em> Published on <em>arXiv</em></p></li>
</ul>
<h2 id="tuning">Tuning</h2>
<h3 id="instruction-tuning">Instruction tuning</h3>
<ul>
<li><p><strong><a href="https://arxiv.org/abs/2106.09685">LoRA: Low-Rank
Adaptation of Large Language Models</a></strong> <em>Edward J. Hu,
Yelong Shen, Phillip Wallis, et al. (2021)</em> Published on
<em>arXiv</em></p></li>
<li><p><strong><a
href="https://papers.nips.cc/paper/2020/file/fc2c7f9a3f3f86cde5d8ad2c7f7e57b2-Paper.pdf">Language
Models are Few-Shot Learners</a></strong> <em>Tom Brown, Benjamin Mann,
Nick Ryder, et al. (2020)</em> Presented at <em>NeurIPS</em></p></li>
</ul>
<h3 id="alignement-tuning">Alignement tuning</h3>
<ul>
<li><strong><a
href="https://papers.nips.cc/paper/2022/hash/17f4c5f98073d1fb95f7e53f5c7fdb64-Abstract.html">Training
language models to follow instructions with human feedback</a></strong>
<em>Long Ouyang, Jeffrey Wu, Xu Jiang, et al. (2022)</em> Presented at
<em>NeurIPS</em></li>
</ul>
<h2 id="existing-llms">Existing LLMs</h2>
<p>Many models are available at the following URLs:<br />
<a href="https://www.nomic.ai/gpt4all">https://www.nomic.ai/gpt4all</a>
and<br />
<a
href="https://huggingface.co/models">https://huggingface.co/models</a>.</p>
<ul>
<li><p><strong><a href="https://arxiv.org/abs/2303.08774">GPT-4
Technical Report</a></strong> <em>OpenAI Team (2024)</em> Published on
<em>arXiv</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/2407.21783">The Llama 3
Herd of Models</a></strong> <em>Meta Team (2024)</em> Published on
<em>arXiv</em></p></li>
<li><p><strong><a
href="https://github.com/tatsu-lab/stanford_alpaca">Stanford Alpaca: An
Instruction-Following LLaMa Model</a></strong> <em>Rohan Taori, Ishaan
Gulrajani, Tianyi Zhang, Yann Dubois, et al. (2023)</em> Published on
<em>GitHub</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/2401.04088">Mixtral of
Experts</a></strong><br />
<em>Mistral AI team (2024)</em><br />
Published on <em>arXiv</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/2310.06825">Mistral
7B</a></strong><br />
<em>Mistral AI team (2023)</em><br />
Published on <em>arXiv</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/">The Lucie-7B LLM and the
Lucie Training Dataset: Open Resources for Multilingual Language
Generation</a></strong> <em>Olivier Gouvert, Julie Hunter, Jérôme
Louradour, Evan Dufraisse, Yaya Sy, Pierre-Carl Langlais, Anastasia
Stasenko, Laura Rivière, Christophe Cerisara, Jean-Pierre Lorré
(2025)</em></p></li>
</ul>
<h2 id="prompt-engineering">Prompt engineering</h2>
<h3 id="icl">ICL</h3>
<ul>
<li><strong>A Survey on In-context Learning</strong> <em>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)</em>
Presented at the <em>Conference on Empirical Methods in Natural Language
Processing (EMNLP)</em> Location: Miami, Florida, USA Published by:
Association for Computational Linguistics</li>
</ul>
<h3 id="cot">CoT</h3>
<ul>
<li><strong><a
href="https://papers.nips.cc/paper/52604-chain-of-thought-prompting-elicits-reasoning-in-large-language-models">Chain-of-Thought
Prompting Elicits Reasoning in Large Language Models</a></strong>
<em>Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, et
al. (2022)</em> Presented at <em>NeurIPS</em></li>
</ul>
<h3 id="rag">RAG</h3>
<ul>
<li><strong><a
href="https://arxiv.org/abs/2312.10997">Retrieval-Augmented Generation
for Large Language Models: A Survey</a></strong> <em>Yunfan Gao, Yun
Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei
Sun, Meng Wang, Haofen Wang (2024)</em> Published on <em>arXiv</em></li>
</ul>
<h2 id="generative-autonomous-agents">Generative Autonomous Agents</h2>
<ul>
<li><p><strong>A Survey on Large Language Model Based Autonomous
Agents</strong> 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)<em> Published in </em>Frontiers of
Computer Science* (Volume 18, Issue 6, Pages</p>
<ol start="186345" type="1">
<li>Publisher: Springer</li>
</ol></li>
<li><p><strong><a
href="https://papers.nips.cc/paper/2023/hash/38154-hugginggpt-solving-ai-tasks-with-chatgpt-and-its-friends-in-hugging-face.pdf">HuggingGPT:
Solving AI Tasks with ChatGPT and its Friends in Hugging
Face</a></strong> <em>Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li,
Weiming Lu, Yueting Zhuang (2023)</em> Presented at <em>Advances in
Neural Information Processing Systems (NeurIPS)</em> Pages: 38154–38180
Publisher: Curran Associates, Inc. Volume: 36</p></li>
<li><p><strong><a
href="https://papers.nips.cc/paper/86759-toolformer-language-models-can-teach-themselves-to-use-tools">Toolformer:
Language Models Can Teach Themselves to Use Tools</a></strong> <em>Timo
Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, et
al. (2023)</em> Presented at <em>NeurIPS</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/2309.02427">Cognitive
Architectures for Language Agents</a></strong><br />
<em>Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L.
Griffiths (2024)</em><br />
Published on <em>arXiv</em></p></li>
</ul>
<h3 id="generative-autonomous-agents-on-the-shelf">Generative Autonomous
Agents on the shelf</h3>
<ul>
<li><p><a href="https://www.langchain.com">LangChain</a> 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.</p></li>
<li><p><a href="https://langchain-ai.github.io/langgraph">LangGraph</a>
is a low-level library for the design of cognitive architecture for
autonomous agents, whose reasoning engine is an LLM.</p></li>
<li><p><a
href="https://github.com/Significant-Gravitas/AutoGPT">AutoGPT</a> is a
platform for the creation, deployment, and management of generative
agents.</p></li>
<li><p><a href="https://github.com/team-openpm/workgpt">WorkGPT</a> is
similar to AutoGPT</p></li>
</ul>
<h2 id="generative-mas">Generative MAS</h2>
<ul>
<li><p><strong><a
href="https://doi.org/10.1057/s41599-024-01235-9">Large language models
empowered agent-based modeling and simulation: A survey and
perspectives</a></strong> **Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan,
Jingtao Ding, Zhilun Zhou, Fengli Xu, Yong Li (2024)* Published in
<em>Humanities and Social Sciences Communications</em>, Volume 11, Issue
1, Pages 1–24</p></li>
<li><p><strong><a
href="https://dl.acm.org/doi/10.1145/3526110.3545617">Social Simulacra:
Creating Populated Prototypes for Social Computing Systems</a></strong>
<em>Joon Sung Park, Lindsay Popowski, Carrie Cai, Meredith Ringel
Morris, Percy Liang, Michael S. Bernstein (2022)</em> Published in
<em>Proceedings of the 35th Annual ACM Symposium on User Interface
Software and Technology</em> Articleno: 74, Pages: 18, Location: Bend,
OR, USA</p></li>
<li><p><strong><a
href="https://dl.acm.org/doi/10.1145/3586184.3594067">Generative Agents:
Interactive Simulacra of Human Behavior</a></strong> <em>Joon Sung Park,
Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang,
Michael S. Bernstein (2023)</em> Published in <em>Proceedings of the
36th Annual ACM Symposium on User Interface Software and Technology</em>
Articleno: 2, Pages: 22, Location: San Francisco, CA, USA, Series: UIST
’23</p></li>
<li><p><strong><a
href="https://openreview.net/forum?id=HywBMyh6JGR">Agentverse:
Facilitating multi-agent collaboration and exploring emergent
behaviors</a></strong> <em>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)</em> Published in <em>The Twelfth International
Conference on Learning Representations (ICLR 2023)</em></p></li>
<li><p><strong><a href="https://arxiv.org/abs/2305.16960">Training
socially aligned language models on simulated social
interactions</a></strong> <em>Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge
Zhang, Denny Zhou, Andrew M. Dai, Diyi Yang, Soroush Vosoughi
(2023)</em> Published on <em>arXiv</em> arXiv:2305.16960</p></li>
<li><p><a href="https://arxiv.org/abs/2307.14984">S3: Social-network
Simulation System with Large Language Model-Empowered Agents</a>**
<em>Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao, Jinghua Piao,
Huandong Wang, Depeng Jin, Yong Li (2023)</em> Published on
<em>arXiv</em> arXiv:2307.14984</p></li>
</ul>
<h3 id="generative-mas-on-the-shelf">Generative MAS on the shelf</h3>
<ul>
<li><p><a href="https://github.com/geekan/MetaGPT">MetaGPT</a> is a
framework for creating generative MAS dedicated to software
development.</p></li>
<li><p><a href="https://github.com/camel-ai/camel">CAMEL</a> proposes a
generative multi-agent framework for accomplishing complex
tasks.</p></li>
<li><p><a href="https://github.com/microsoft/autogen">AutoGen</a> is a
versatile open-source framework for creating generative multi-agent
systems.</p></li>
</ul>
<h2 id="authors">Authors</h2>
<p>Maxime MORGE</p>
<h2 id="license">License</h2>
<p>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.</p>
<p>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.</p>
<p>You should have received a copy of the GNU General Public License
along with this program. If not, see <a
href="http://www.gnu.org/licenses/"
class="uri">http://www.gnu.org/licenses/</a>.</p>
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