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https://arxiv.org/abs/2405.05248 LLMs with Personalities in Multi-issue Negotiation GamesPowered by large language models (LLMs), AI agents have become capable of many human tasks. Using the most canonical definitions of the Big Five personality, we measure the ability of LLMs to negotiate within a game-theoretical framework, as well as methodarxiv.org 이 논문은 LLM을 성격적 요소로 나눠서 협상을 진행하고, 그 결과를 보는 논..

https://arxiv.org/abs/2404.01037 ARAGOG: Advanced RAG Output GradingRetrieval-Augmented Generation (RAG) is essential for integrating external knowledge into Large Language Model (LLM) outputs. While the literature on RAG is growing, it primarily focuses on systematic reviews and comparisons of new state-of-the-art (SoTA)arxiv.org 이 논문은 RAG 기술을 체계적으로 비교하며, 검색 정확도와 답변 유사성이라는 명확한 지표를 통해 RAG 시스템의 성..

https://arxiv.org/abs/2403.10081 DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language ModelsDynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). There are two key elements of this paradigm: identifying the optimal moment to activate thearxiv.o..

https://arxiv.org/abs/2402.05131 Financial Report Chunking for Effective Retrieval Augmented GenerationChunking information is a key step in Retrieval Augmented Generation (RAG). Current research primarily centers on paragraph-level chunking. This approach treats all texts as equal and neglects the information contained in the structure of documents. We proarxiv.org 일단 제가 찾던 논문 중 하나입니다!Chunking을..

https://arxiv.org/abs/2406.17526 LumberChunker: Long-Form Narrative Document SegmentationModern NLP tasks increasingly rely on dense retrieval methods to access up-to-date and relevant contextual information. We are motivated by the premise that retrieval benefits from segments that can vary in size such that a content's semantic independencearxiv.org 이 논문은 LLM을 통해 청크를 분리하네요그런데 이렇게 되면 리소스가 너무 과하..

https://arxiv.org/abs/2406.07348 DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-AnsweringRetrieval-Augmented Generation (RAG) has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA). RAG expands the query context by incorporating external knowledge bases to enhaarxiv.org..

https://arxiv.org/abs/2402.01737 Assistive Large Language Model Agents for Socially-Aware Negotiation DialoguesWe develop assistive agents based on Large Language Models (LLMs) that aid interlocutors in business negotiations. Specifically, we simulate business negotiations by letting two LLM-based agents engage in role play. A third LLM acts as a remediator agent tarxiv.org 이 논문에는 LLM을 중재자 혹은 판단..

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..

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..

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..