ABSTRACT

Recently, computer scientists have developed large language models (LLMs) by training prediction models with large-scale language corpora and human reinforcements. The LLMs have become one promising way to implement artificial intelligence with accuracy in various fields. Interestingly, recent LLMs possess emergent functional features that emulate sophisticated human cognition, especially in-context learning and the chain of thought, which were unavailable in previous prediction models. In this paper, I will examine how LLMs might contribute to moral education and development research. To achieve this goal, I will review the most recently published conference papers and ArXiv preprints to overview the novel functional features implemented in LLMs. I also intend to conduct brief experiments with ChatGPT to investigate how LLMs behave while addressing ethical dilemmas and external feedback. The results suggest that LLMs might be capable of solving dilemmas based on reasoning and revising their reasoning process with external input. Furthermore, a preliminary experimental result from the moral exemplar test may demonstrate that exemplary stories can elicit moral elevation in LLMs as do they among human participants. I will discuss the potential implications of LLMs on research on moral education and development with the results.

摘要

最近，计算机科学家通过使用大规模语言语料库和人类强化训练预测模型，开发了大型语言模型（LLM）。法学硕士已成为在各个领域准确实施人工智能的一种有前景的方式。有趣的是，最近的法学硕士拥有模拟复杂人类认知的新兴功能特征，特别是情境学习和思维链，这是以前的预测模型所不具备的。在本文中，我将研究法学硕士如何为道德教育和发展研究做出贡献。为了实现这一目标，我将回顾最近发表的会议论文和 ArXiv 预印本，以概述法学硕士中实现的新颖功能特性。我还打算使用 ChatGPT 进行简短的实验，以调查法学硕士在解决道德困境和外部反馈时的行为方式。结果表明，法学硕士可能能够基于推理解决困境，并通过外部输入修改其推理过程。此外，道德典范测试的初步实验结果可能表明，模范故事可以引起法学硕士的道德提升，就像人类参与者一样。我将讨论法学硕士对道德教育和发展研究的潜在影响及其结果。道德典范测试的初步实验结果可能表明，与人类参与者一样，典范故事可以引起法学硕士的道德提升。我将讨论法学硕士对道德教育和发展研究的潜在影响及其结果。道德典范测试的初步实验结果可能表明，与人类参与者一样，典范故事可以引起法学硕士的道德提升。我将讨论法学硕士对道德教育和发展研究的潜在影响及其结果。