将大模型视为技术符号机器,揭示知识幻觉的深层机制
Beyond Epistemia: Epistemic Schizologia and Large Language Models as Techno-Semiotic Machines
- 把大模型看作自动完成书写符号过程的技术符号系统
- 提出'认识分裂症'概念,揭示符号表达与解释链条的断裂
- 强调人机协同实践中的可追溯性与责任共担,适合关注AI伦理者
Quattrociocchi等人指出,大语言模型流畅输出可能使语言似真性替代认知评估,造成他们所谓的'认识论状态':拥有知识的体验却未履行判断所需的实践。本文接受此诊断,但挑战其解释框架——该框架将具身化、社会嵌入的人类认知者与孤立的生成模型对比,将认知正当性归因于自主主体的内在能力。本文借鉴Carlo Sini关于实践、写作、符号与技术的哲学,主张将大语言模型理解为一种'技术符号机器',它通过从人类写作积淀的档案中生成合乎语境的语言配置,自动化了书面符号过程的一个阶段。由此视角,'认识论状态'是更广泛现象——我们称之为'认识分裂症'——的后果:符号作为语言实现的表达,与符号作为社会嵌入的解释、证据、批判、验证和责任回路中的节点之间,出现社会技术裂隙。这一裂隙由'意象闭合'强化,即看似合理的延续被呈现为认知结果的终局;同时受算法权威与认识自我误认推动。因此,关键单位并非模型本身,而是包含提示、解读、验证、质疑、使用及产生后果的完整实践。此重构在保持语言生产与负责任理解区别的同时,确立了一个以可检视谱系、可争议性、分布式责任、认知能动性,以及对人机混合实践的评估为中心的设计方案。
原文摘要 · Abstract (English)
Quattrociocchi and colleagues warn that the fluent outputs of large language models may allow linguistic plausibility to substitute for epistemic evaluation, producing the condition they call *Epistemia*: the experience of possessing knowledge without undertaking the practices through which judgment would ordinarily be warranted. This article accepts that diagnosis but challenges its explanatory framework, which compares an embodied, socially situated human knower with an isolated generative model thereby locating epistemic legitimacy in capacities internal to autonomous agents. Drawing on Carlo Sini's philosophy of practices, writing, signs, and technics, we propose instead to understand a large language model (LLM) as a *techno-semiotic machine* that automates a phase of written semiosis by producing plausible linguistic configurations from the sedimented archive of human writing. From this perspective, *Epistemia* is one consequence of a broader phenomenon that we call *epistemic schizologia*: the socio-technical cleavage between signs as linguistically accomplished expressions and signs as moments within socially embedded circuits of interpretation, evidence, criticism, verification, and responsibility. This cleavage is reinforced by *eikotic closure*, through which a plausible continuation is presented with the finality of an epistemic result, and by algorithmic authority and epistemic self-misrecognition. The relevant unit is therefore not the model alone but the complete practice in which generated inscriptions are prompted, interpreted, verified, contested, used, and made consequential. This reframing preserves the distinction between linguistic production and responsible understanding while grounding a design programme centred on inspectable genealogy, contestability, distributed responsibility, epistemic agency, and the evaluation of hybrid human--AIpractices.
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