공대생 도전 일지

https://arxiv.org/abs/2401.03568 Agent AI: Surveying the Horizons of Multimodal InteractionMulti-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents within physical and virtual environments. At present, systems leverage existingarxiv.org 이 논문은 LLM 뿐많이 아니라 VLM을 활용하여 AGI 도달하기 위한 A..

https://arxiv.org/abs/2308.01552 InterAct: Exploring the Potentials of ChatGPT as a Cooperative AgentThis research paper delves into the integration of OpenAI's ChatGPT into embodied agent systems, evaluating its influence on interactive decision-making benchmark. Drawing a parallel to the concept of people assuming roles according to their unique strengtarxiv.org ReAct를 발전시킨 논문이네요2024.11.26 - [..

https://arxiv.org/abs/2402.05863 How Well Can LLMs Negotiate? NegotiationArena Platform and AnalysisNegotiation is the basis of social interactions; humans negotiate everything from the price of cars to how to share common resources. With rapidly growing interest in using large language models (LLMs) to act as agents on behalf of human users, such LLM agarxiv.org 음 이 논문은 그래도 LLM까지는 갔지만 LLM을 학습하거..

https://arxiv.org/abs/2208.07004?utm_source=chatgpt.com AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-NComprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooper..

https://arxiv.org/abs/2307.12856 A Real-World WebAgent with Planning, Long Context Understanding, and Program SynthesisPre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context lengtarxiv.org WebAgent는 실제 ..

https://arxiv.org/abs/2406.04151 AgentGym: Evolving Large Language Model-based Agents across Diverse EnvironmentsBuilding generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community. Large language models (LLMs) are considered a promising foundation to build such agents due to their generarxiv.org 여기서는 직접 LLM을 학습하는 A..

https://arxiv.org/abs/2310.05029 Walking Down the Memory Maze: Beyond Context Limit through Interactive ReadingLarge language models (LLMs) have advanced in large strides due to the effectiveness of the self-attention mechanism that processes and compares all tokens at once. However, this mechanism comes with a fundamental issue -- the predetermined context windowarxiv.org 이 논문은 트리 구조를 통해 짧게 요약해..

https://arxiv.org/abs/2309.08112 Empowering Private Tutoring by Chaining Large Language ModelsArtificial intelligence has been applied in various aspects of online education to facilitate teaching and learning. However, few approaches has been made toward a complete AI-powered tutoring system. In this work, we explore the development of a full-fledarxiv.org 오 LLM이 선생님이 된다!Memory를 활용하여 아는 것, 모르는 ..

https://arxiv.org/abs/2307.07924 ChatDev: Communicative Agents for Software DevelopmentSoftware development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing. However, the deep leaarxiv.org 이 논문도 이전에 보았던 마인크레프트 Agent와 비슷하게 Long term, S..

항목내용논문의 주제LLM의 협상 대화에서의 다면적 능력을 체계적으로 평가.연구 목표- 협상 대화에서 LLM의 이해, 주석, 파트너 모델링, 응답 생성 능력을 평가.- LLM을 활용한 협상 시스템의 가능성과 한계를 탐구.데이터셋CRA, DND, CA(CaSiNo), JI(Job Interview) 등 총 4개 데이터셋 사용.- Multi-Issue Bargaining Task(MIBT) 기반으로 협상 시나리오 설계.평가 방식- 태스크 설계: 35개 태스크로 세분화(이해, 주석, 파트너 모델링, 응답 생성).- 시간 단계: 협상 시작(Start), 진행(During), 종료(End)로 구분.- 객관적(정답 존재) 및 주관적(심리 상태 추론) 평가로 나눔.비교 모델GPT-4, GPT-3.5, Mistral-7..