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Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation - 논문 리뷰

https://arxiv.org/abs/2309.17234 Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive NegotiationThere is an growing interest in using Large Language Models (LLMs) in multi-agent systems to tackle interactive real-world tasks that require effective collaboration and assessing complex situations. Yet, we still have a limited understanding of LLMs' commarxiv.org 이 논문은 대규모 언어 모..

Chat QA1, Chat QA2 정리하면서 발전 가능성, 개선 점 생각해보기

2024.12.23 - [인공지능/논문 리뷰 or 진행] - ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰 ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰https://arxiv.org/abs/2401.10225 ChatQA: Surpassing GPT-4 on Conversational QA and RAGIn this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (Q..

Using Large Language Models for Humanitarian Frontline Negotiation: Opportunities and Considerations - 논문 리뷰

https://arxiv.org/abs/2405.20195 Using Large Language Models for Humanitarian Frontline Negotiation: Opportunities and ConsiderationsHumanitarian negotiations in conflict zones, called \emph{frontline negotiation}, are often highly adversarial, complex, and high-risk. Several best-practices have emerged over the years that help negotiators extract insights from large datasets to navigatarxiv.org..