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Modelling Political Coalition Negotiations Using LLM-based Agents - 논문 리뷰

https://arxiv.org/abs/2402.11712 Modelling Political Coalition Negotiations Using LLM-based AgentsCoalition negotiations are a cornerstone of parliamentary democracies, characterised by complex interactions and strategic communications among political parties. Despite its significance, the modelling of these negotiations has remained unexplored with tharxiv.org 협상이긴 한데 뭔가 부족한 느낌입니다.그래도 결과를 높이려고 ..

Sentiment Analysis through LLM Negotiations - 논문 리뷰

https://arxiv.org/abs/2311.01876 Sentiment Analysis through LLM NegotiationsA standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning. This framework suffers the key disadvantage that the single-turn output generated by a single LLM marxiv.org 감정 분석의 정확도를 올리기 위해 협상? 토론? 을 사용했다는 논문입니다.제가 찾던 완전한 협상 논문..

Evaluating Language Model Agency through Negotiations - 논문 리뷰

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 ..

Let's Negotiate! A Survey of Negotiation Dialogue Systems - 논문 리뷰

https://arxiv.org/abs/2402.01097 Let's Negotiate! A Survey of Negotiation Dialogue SystemsNegotiation is a crucial ability in human communication. Recently, there has been a resurgent research interest in negotiation dialogue systems, whose goal is to create intelligent agents that can assist people in resolving conflicts or reaching agreementsarxiv.org 이 논문은 협상 대화 시스템의 발전을 체계적으로 정리하며, 방법론, 데이터셋..

Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care - 논문 리뷰

https://arxiv.org/abs/2302.00096 Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health CareArtificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support interface that provides interpretable treatment recommendations for ..

https://arxiv.org/abs/2410.14383 MARLIN: Multi-Agent Reinforcement Learning Guided by Language-Based Inter-Robot NegotiationMulti-agent reinforcement learning is a key method for training multi-robot systems over a series of episodes in which robots are rewarded or punished according to their performance; only once the system is trained to a suitable standard is it deployed inarxiv.org 이 논문은 협상에..

https://publications.cispa.de/articles/journal_contribution/LLM-Deliberation_Evaluating_LLMs_with_Interactive_Multi-Agent_Negotiation_Games_/25233028/1?file=44571847 LLM-Deliberation: Evaluating LLMs with Interactive Multi-Agent Negotiation Games.There is a growing interest in using Large Language Models (LLMs) as agents to tackle real-world tasks that may require assessing complex situations. Y..

https://arxiv.org/abs/2405.16751 LLM-Based Cooperative Agents using Information Relevance and Plan ValidationWe address the challenge of multi-agent cooperation, where agents achieve a common goal by interacting with a 3D scene and cooperating with decentralized agents under complex partial observations. This involves managing communication costs and optimizing iarxiv.org 여기선 동적으로 변화하는 환경에서 LLM이..

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...