From 7c72e9d43dcd34e01030ac955105511eac5dcad1 Mon Sep 17 00:00:00 2001 From: Maxime MORGE <maxime.morge@univ-lille.fr> Date: Mon, 27 Jan 2025 14:08:29 +0100 Subject: [PATCH] LLM4AAMAS : add license --- README.html | 272 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 272 insertions(+) create mode 100644 README.html diff --git a/README.html b/README.html new file mode 100644 index 0000000..800e370 --- /dev/null +++ b/README.html @@ -0,0 +1,272 @@ +<?xml version="1.0" encoding="UTF-8" ?> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" + "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> + +<html xmlns="http://www.w3.org/1999/xhtml"> + +<head> +<title>README.html</title> +<meta http-equiv="Content-Type" content="text/html;charset=utf-8"/> + +</head> + +<body> + +<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> + +</body> +</html> -- GitLab