LLM生成内容看似有意识，实则无真正意图与责任能力。
Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models
- 指出大模型的输出是数据驱动的概率映射，非自主选择。
- 强调道德责任需基于内在意图与自我承诺，而模型不具备此条件。
- 适合关注AI伦理、技术哲学的读者深入理解代理性边界。
近期大语言模型的发展引发了关于其是否具备代理性或可视为道德主体的讨论。本文认为这些观点存在误解。我们主张，道德责任要求建立在内在意图与自我承担行为的基础之上的承诺型代理性，而这正是责任相关自由意志的形式。尽管大模型能生成连贯且可规范评价的输出，但其运作本质是通过数据学习到的确定性概率输入-输出映射。其表面意图是衍生而非内在的，输出既未被视作承诺，也非由理由引导。随机采样带来的变异性并不构成真正的选择或作者权。本文回应了来自意向立场、功能主义、相容论以及模型输出中存在道德推理等反驳观点，认为均不足以确立真实代理性。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have prompted claims that such systems exhibit agency or qualify as moral agents. This paper argues that these attributions are misguided. We maintain that moral responsibility requires commitment-bearing agency grounded in intrinsic intentionality and self-attributed action, and that such agency constitutes the form of free will relevant to responsibility. Although LLMs generate coherent and normatively evaluable outputs, their operation is fully characterized by probabilistic input-output mappings learned from data. Their apparent intentionality is derived rather than intrinsic, and their outputs are neither owned as commitments nor guided by reasons. Variability introduced by stochastic sampling does not amount to choice or authorship. We address objections from the intentional stance, functionalism, compatibilism, and the presence of moral reasoning in model outputs, arguing that none suffice to establish genuine agency.
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。