This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compared directly to those produced by graduate-level human participants. By focusing on two Nobel Prize-winning short stories, 'Nine Chapters' by Han Kang, the 2024 laureate, and 'Friendship' by Jon Fosse, the 2023 laureate, the research explores the extent to which AI can engage with complex literary elements such as thematic analysis, intertextuality, cultural and historical contexts, linguistic and structural innovations, and character development. Given the Nobel Prize's prestige and its emphasis on cultural, historical, and linguistic richness, applying LLMs to these works provides a deeper understanding of both human and AI approaches to interpretation. The study uses qualitative and quantitative evaluations of coherence, creativity, and fidelity to the text, revealing the strengths and limitations of AI in tasks typically reserved for human expertise. While LLMs demonstrate strong analytical capabilities, particularly in structured tasks, they often fall short in emotional nuance and coherence, areas where human interpretation excels. This research underscores the potential for human-AI collaboration in the humanities, opening new opportunities in literary studies and beyond.

翻译：本研究探讨了先进大型语言模型（LLMs），特别是o1模型，在文学分析领域的能力。我们将这些模型的输出结果与研究生水平的人类参与者的分析成果进行直接比较。通过聚焦于两篇诺贝尔奖获奖短篇小说——2024年获奖者韩江的《九章》与2023年获奖者约恩·福瑟的《友谊》，本研究深入探究了人工智能在多大程度上能够处理复杂的文学元素，例如主题分析、互文性、文化与历史语境、语言与结构创新以及人物发展。鉴于诺贝尔奖的崇高声望及其对文化、历史与语言丰富性的强调，将LLMs应用于这些作品有助于更深入地理解人类与人工智能在文本阐释路径上的异同。本研究采用定性与定量相结合的方法，从连贯性、创造性及文本忠实度等方面进行评估，揭示了人工智能在通常由人类专家主导的任务中所展现的优势与局限。尽管LLMs展现出强大的分析能力，尤其在结构化任务中，但在情感细微差别与整体连贯性方面往往存在不足，而这些正是人类阐释的卓越之处。本研究强调了人机协作在人文领域的潜力，为文学研究及其他相关领域开辟了新的机遇。