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https://arxiv.org/abs/2305.10142 Improving Language Model Negotiation with Self-Play and In-Context Learning from AI FeedbackWe study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because if LLMs were able to improve each other, it would imply thearxiv.org 음 여기선 이..

https://arxiv.org/abs/2308.08155 AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent ConversationAutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combiarxiv.org 이건 아래 논문과 엄청 비슷한 느낌이네요2024...

https://arxiv.org/abs/1706.05125 Deal or No Deal? End-to-End Learning for Negotiation DialoguesMuch of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interestingarxiv.org 아직 Transformer 구조도 없어 RNN기반의 GRU 모델을 통..