From bc904b92cc3df0da454443a87cba2c85533d9096 Mon Sep 17 00:00:00 2001 From: Maxime MORGE <maxime.morge@univ-lille.fr> Date: Mon, 27 Jan 2025 14:09:15 +0100 Subject: [PATCH] LLM4AAMAS : rm HTML --- README.html | 272 ---------------------------------------------------- 1 file changed, 272 deletions(-) delete mode 100644 README.html diff --git a/README.html b/README.html deleted file mode 100644 index 800e370..0000000 --- a/README.html +++ /dev/null @@ -1,272 +0,0 @@ -<?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