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https://arxiv.org/abs/2401.04536 Evaluating Language Model Agency through NegotiationsWe introduce an approach to evaluate language model (LM) agency using negotiation games. This approach better reflects real-world use cases and addresses some of the shortcomings of alternative LM benchmarks. Negotiation games enable us to study multi-turnarxiv.org 오 딱 제가 생각했던 Agent의 평가를 어떻게 해야 멀티턴에 적합하게, 벤치마크 ..

이전 글에서 이어진다 하긴 애매하지만 제 정보를 저장해놓고 보기 편하게 하려고 만드는 저장소입니다...2024.12.10 - [인공지능/Agent] - Negotiation AI 1 정보의 싸" data-og-host="yoonschallenge.tistory.com" data-og-source-url="https://yoonschallenge.tistory.com/907" data-og-url="https://yoonschallenge.tistory.com/907" data-og-image="https://scrap.kakaocdn.net/dn/bLohT6/hyXKubelU7/khviVLBLaPUc18X8hbkCLk/img.png?width=800&height=464&face=0_0_800_464,ht..

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...