• Title/Summary/Keyword: 대형언어모형

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Enhancing Empathic Reasoning of Large Language Models Based on Psychotherapy Models for AI-assisted Social Support (인공지능 기반 사회적 지지를 위한 대형언어모형의 공감적 추론 향상: 심리치료 모형을 중심으로)

  • Yoon Kyung Lee;Inju Lee;Minjung Shin;Seoyeon Bae;Sowon Hahn
    • Korean Journal of Cognitive Science
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    • v.35 no.1
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    • pp.23-48
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    • 2024
  • Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

Modeling of Spectral Waves using a Mild Slope Equation of Hyperbolic Type (쌍곡선형 완경사 방정식을 이용한 스펙트럼 불규칙파 예측 모형 개발)

  • Kim, Dong Hee;Lee, Jung Lyul
    • Proceedings of the Korea Water Resources Association Conference
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    • 2016.05a
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    • pp.163-163
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    • 2016
  • 선박의 대형화 등으로 인한 세계적인 항만들이 하역능력의 제고를 위하여 선박의 대형화, 고속화, 전용선화에 있어 큰 움직임을 보이고 있다. 또한 항만의 연간 작업가능일수 확보를 위하여 신항만 건설시 항 내 정온도 향상을 위하여 최적의 방파제 배치 및 최선의 소파기술에 대한 연구지원을 아끼지 않고 있다. 이뿐 아니라 최근 파랑 수치모형의 정확성이 향상되고 계산시간이 단축됨으로써 각 격자 상에 입력된 수심정보와 입사경계에서의 입사정보 경계면에서의 경계(반사율) 정보로부터 손쉽게 천해파랑 정보를 산출할 수 있게 되었다. 본 연구에서는 스펙트럼을 통해 각각의 파고와 주기를 추출하였으며, 쌍곡선형 완경사 방정식을 수치 해석하여 불규칙파의 설계파를 산정하였다. 또한 Matlab을 사용하여 전 프로그램이 toolbox화 됨으로써 운영하는데 편리하고 특히 다양한 지형의 적용에 용이하게 되었다. Matlab은 다른 언어와 달리 전 프로그램이 vectorizing 되어 계산시간이 상당히 단축되었다. 본 연구를 통해 실무자들이 항만이나 어항 등 연안해역 개발시 유의파고를 사전에 예측하여 연안해역 개발하는 데 큰 도움이 되리라 기대한다.

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Literature Review of AI Hallucination Research Since the Advent of ChatGPT: Focusing on Papers from arXiv (챗GPT 등장 이후 인공지능 환각 연구의 문헌 검토: 아카이브(arXiv)의 논문을 중심으로)

  • Park, Dae-Min;Lee, Han-Jong
    • Informatization Policy
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    • v.31 no.2
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    • pp.3-38
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    • 2024
  • Hallucination is a significant barrier to the utilization of large-scale language models or multimodal models. In this study, we collected 654 computer science papers with "hallucination" in the abstract from arXiv from December 2022 to January 2024 following the advent of Chat GPT and conducted frequency analysis, knowledge network analysis, and literature review to explore the latest trends in hallucination research. The results showed that research in the fields of "Computation and Language," "Artificial Intelligence," "Computer Vision and Pattern Recognition," and "Machine Learning" were active. We then analyzed the research trends in the four major fields by focusing on the main authors and dividing them into data, hallucination detection, and hallucination mitigation. The main research trends included hallucination mitigation through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), inference enhancement via "chain of thought" (CoT), and growing interest in hallucination mitigation within the domain of multimodal AI. This study provides insights into the latest developments in hallucination research through a technology-oriented literature review. This study is expected to help subsequent research in both engineering and humanities and social sciences fields by understanding the latest trends in hallucination research.