这篇论文对 AI 伦理和哲学感兴趣的读者来说是一剂清醒剂——它拆解了 LLM 是否具有道德主体地位的争论核心，做 AI 安全或伦理研究的团队值得一读，看完会对“模型是否有意识”有更清晰的认识。
这篇论文反驳了大型语言模型（LLM）具备能动性或道德主体地位的观点。作者认为，道德责任需要基于内在意向性和自我归因行动的承诺性能动性，而 LLM 的操作完全由从数据中学习的概率输入输出映射决定。它们的表面意向性是派生的而非内在的，输出既不被视为承诺，也不受理由引导。随机采样引入的变异性并不等同于选择或作者身份。论文回应了意向立场、功能主义、相容论和模型输出中的道德推理等反对意见，认为这些都不足以确立真正的能动性。
Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models
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.