把大模型看作符号操作者,而非思考机器。
Not Minds, but Signs: Reframing LLMs through Semiotics
- 用符号学视角替代认知主义,强调模型处理语言符号的机制。
- 模型通过概率关联重组语言,输出可被解读的文本,非真正理解。
- 适合对语言、意义和人工智能伦理感兴趣的读者。
本文挑战将大语言模型(LLMs)视为认知系统的主流观点,主张以符号学视角重新理解这些模型在符号操作与意义建构中的作用。我们提出,大模型的核心功能并非理解语言或模拟人类思维,而是基于概率关联重新组合、重构语境并传播语言形式。通过从认知主义转向符号学框架,我们避免了拟人化,并更精准地把握大模型如何参与文化过程——它们不思考,而是生成可被解读的文本,激发意义协商。文章通过理论分析与实例展示,说明大模型作为符号主体,其输出可被视为解释性行为,开放于语境互动与批判反思。应用涵盖文学、哲学、教育与文化生产,强调其在创造力、对话与批判探究中的工具价值。符号范式凸显意义的语境性、偶然性与社会嵌入性,为研究与使用大模型提供更严谨且具伦理意识的框架。最终,这一视角将大模型重定义为符号生态中的技术参与者:它们无心智,却重塑我们读写与建构意义的方式,迫使我们重新审视语言、诠释及人工系统在知识生产中的角色。
原文摘要 · Abstract (English)
This paper challenges the prevailing tendency to frame Large Language Models (LLMs) as cognitive systems, arguing instead for a semiotic perspective that situates these models within the broader dynamics of sign manipulation and meaning-making. Rather than assuming that LLMs understand language or simulate human thought, we propose that their primary function is to recombine, recontextualize, and circulate linguistic forms based on probabilistic associations. By shifting from a cognitivist to a semiotic framework, we avoid anthropomorphism and gain a more precise understanding of how LLMs participate in cultural processes, not by thinking, but by generating texts that invite interpretation. Through theoretical analysis and practical examples, the paper demonstrates how LLMs function as semiotic agents whose outputs can be treated as interpretive acts, open to contextual negotiation and critical reflection. We explore applications in literature, philosophy, education, and cultural production, emphasizing how LLMs can serve as tools for creativity, dialogue, and critical inquiry. The semiotic paradigm foregrounds the situated, contingent, and socially embedded nature of meaning, offering a more rigorous and ethically aware framework for studying and using LLMs. Ultimately, this approach reframes LLMs as technological participants in an ongoing ecology of signs. They do not possess minds, but they alter how we read, write, and make meaning, compelling us to reconsider the foundations of language, interpretation, and the role of artificial systems in the production of knowledge.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。