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In this paper, we explore the potential of Large Language Models (LLMs) Agents in playing the strategic social deduction game, Resistance Avalon.
Agents that reduce work and information overload
Pattie Maes · 1994
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Intelligent Agents V, Agent Theories, Architectures, and Languages, 5th International Workshop, ATAL ’98, Paris, France, July 4-7, 1998, Proceedings , volume 1555 of Lecture Notes in Computer Science , 1999. Springer
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Llama-adapter v2: Parameter-efficient visual instruction model
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AVIS: autonomous visual information seeking with large language models
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https://github.com/Significant-Gravitas/Auto-GPT
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https://github.com/yoheinakajima/babyagi
Github repository of babyagi
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Agentbench: Evaluating llms as agents
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Wiki-page of "the resistance (game)"
Wiki
Cited in the paper.
Llama-adapter: Efficient fine-tuning of language models with zero-init attention
Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and Yu Qiao
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Multimodal chain-of-thought reasoning in language models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola
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