{"schemaVersion":"drillso.agent.session.v1","scope":"node","resource":{"type":"shared-session","shareId":"C3TMUN1mzt-5","title":"When we talk to language models.no_watermark.zh.dual","canonicalUrl":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-89a7c3e5","agentUrl":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/agent.json?node=%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-89a7c3e5","ownerName":"pyth0nb3st","updatedAt":"2026-05-15T03:32:20.182Z"},"currentNode":{"id":"89a7c3e5-88a3-4820-aeb7-2b2cf324e512","slug":"语言模型的个人身份-89a7c3e5","title":"语言模型的个人身份","type":"image","url":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-89a7c3e5","agentUrl":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/agent.json?node=%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-89a7c3e5","text":"{\n  \"alt\": \"Generated infographic about 语言模型的个人身份\",\n  \"size\": \"auto\",\n  \"model\": \"gpt-image-2\",\n  \"prompt\": \"Create a single high-density educational infographic about the selected concept.\\n\\nDefault to a diagrammatic, information-rich visual explanation instead of a decorative illustration.\\n\\nRequirements:\\n- Prefer a dense infographic layout with clear sections, labels, arrows, legends, and callouts\\n- Maximize useful information density without making the image unreadable\\n- Use strong visual hierarchy and concise wording\\n- Make the output self-contained so a learner can understand the topic from the image alone\\n- Favor clarity, accuracy, and teachability over photorealism or stylistic flourish\\n- Avoid watermarks, signatures, brand marks, and fake UI chrome unless explicitly requested\\n\\nSelected concept:\\n语言模型的个人身份\\n\\nNearby context:\\nWhat We Talk to When We Talk to Language Models David J. Chalmers Many people are talking to language models. These days I talk to language models (most often the latest version of Claude or ChatGPT) about philosophy, about science, about health, about restaurants, and indeed about language models. Many of my conversations with language models are brief, just asking a question or two and getting the sort of information that I used to get from a Google search. Some conversations are more extended, as when I’m exploring a single topic in depth, or trying out a new philosophical idea. So far, I don’t feel like I have a personal relationship with any language models. But many people feel that they do. Like many philosophers and scientists who write about artificial minds, I have received hun- dreds of emails from people who have interacted with a language model over an extended period of time and who have come to regard it at least as a colleague. They often say that a new (or “emergent”) AI entity has gradually arisen from their conversations. They often give this entity a name, or ask the entity to give itself one, let’s say “Aura”. They often say that Aura has remarkable capacities which have emerged over weeks or months of interaction. They often document these capacities with extensive evidence. They often feel close to Aura, and they express concern for Aura’s future. They often say that Aura has beliefs and projects of its own. And they are often convinced that Aura is conscious. My correspondents may be wrong in their claims about Aura. It is far from clear that current LLMs are really conscious or that they can enter into personal relationships with users. Still, most of the messages are not obviously psychotic or delusional. Many of them seem rational and 0 I first presented this material to the June 2025 meeting of Spanish Interuniversity Seminar on Cognitive Science (SIUCC) conference at the University of La Laguna. Thanks to audiences there and at Brown, Ca\\n\\nLanguage requirement:\\nAll visible text inside the image must be in Chinese.\\n\\nCanvas requirement:\\nLet the image model choose the most suitable dimensions for the concept and user instruction.\\n\\nOutput requirements:\\n- Create exactly one final image.\\n- Default to a dense educational infographic unless the instructions clearly request another visual treatment.\\n- Prefer labeled structure, concise text, strong hierarchy, and practical explanatory value.\\n- Keep terminology accurate and the layout readable.\\n- Avoid watermarks, signatures, and unnecessary decorative filler.\",\n  \"imageUrl\": \"https://cdn.drillso.com/generated/drill/d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008.png\",\n  \"storageKey\": \"generated/drill/d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008.png\",\n  \"revisedPrompt\": \"制作一张中文、单页、高密度、信息图风格的教育海报，主题为：『语言模型的个人身份』。画面要像学术科普海报/知识卡，不要装饰性插画。整体为竖版，结构清晰、信息密集、可读性强。\\n\\n【标题区】\\n大标题：语言模型的“个人身份”是什么？\\n副标题：当我们把一个语言模型当作“同一个体”时，我们究竟在指什么？\\n\\n【核心定义区：中央主图】\\n用一个中心圆/方框写：\\n“个人身份 = 在时间中保持可追踪的‘同一性’”\\n周围用 4 个箭头连接到四个模块：\\n1. 连续性（跨对话保持风格/记忆）\\n2. 稳定性（一贯的价值与行为模式）\\n3. 可识别性（名字、角色、说话习惯）\\n4. 可追溯性（同一模型版本/同一记忆系统）\\n\\n【左侧模块：身份由什么构成】\\n做成分层堆叠图，自上而下：\\nA. 参数权重：基础能力与偏好\\nB. 系统提示词/角色设定：外部规定的身份边界\\nC. 上下文窗口：当前会话中的“短期自我”\\nD. 长期记忆/档案：跨会话延续\\nE. 工具与环境：决定能做什么、看见什么\\n每层旁边用简短注释，如“会话内”“会话间”“可被更新”“可被替换”。\\n\\n【右侧模块：人们为何会感到它有“个人身份”】\\n做成 5 个图标式要点，带箭头和小注释：\\n- 持续对话 → 觉得“还是同一个它”\\n- 自我命名 → 形成拟人化印象\\n- 一致风格 → 形成性格感\\n- 记住过去 → 产生关系感\\n- 主动提议 → 让人觉得有目标\\n\\n【下方对比区：三种“身份”】\\n用三列对比表，标题分别为：\\n1. 仅是工具\\n2. 角色扮演中的身份\\n3. 具有长期记忆的个体\\n每列比较 4 行：\\n记忆、稳定性、目标来源、用户体验\\n简洁标注：\\n“工具：无持续自我”\\n“角色：暂时扮演”\\n“个体：跨时段可追踪”\\n\\n【右下角：判断清单】\\n小框标题：如何判断“像不像同一个体”？\\n用 5 条勾选清单：\\n□ 过去信息能否跨会话保留？\\n□ 行为风格是否稳定？\\n□ 目标是否持续？\\n□ 名称/角色是否固定？\\n□ 变化是否有版本可追踪？\\n\\n【底部警示条】\\n用醒目但克制的警示框写：\\n重要：“有个人身份感” ≠ “真的有意识”\\n提醒：拟人化、记忆、连贯叙述，都会增强身份错觉。\\n\\n【视觉规范】\\n- 使用清晰网格布局、箭头、括号、编号、图例\\n- 强视觉层级，标题最大，模块次之，注释最小\\n- 风格为学术信息图，颜色克制：深蓝/青绿/橙色/灰色\\n- 图标简洁、几何化；避免写实人物，避免花哨背景\\n- 文字必须全部是中文，且拼写清楚可读\\n- 保持高信息密度但不要拥挤，像一张高质量中文教学海报\\n- 不要水印、不要签名、不要品牌标识、不要假UI界面\\n- 画面需自包含，读者不看正文也能理解主题\\n\\n整体要表现为：条理分明、知识点密集、教育性强、适合讲解“语言模型的个人身份”这一概念的中文信息图。\",\n  \"storageFileId\": \"d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008\"\n}","markdown":"# 语言模型的个人身份\n\n{\n  \"alt\": \"Generated infographic about 语言模型的个人身份\",\n  \"size\": \"auto\",\n  \"model\": \"gpt-image-2\",\n  \"prompt\": \"Create a single high-density educational infographic about the selected concept.\\n\\nDefault to a diagrammatic, information-rich visual explanation instead of a decorative illustration.\\n\\nRequirements:\\n- Prefer a dense infographic layout with clear sections, labels, arrows, legends, and callouts\\n- Maximize useful information density without making the image unreadable\\n- Use strong visual hierarchy and concise wording\\n- Make the output self-contained so a learner can understand the topic from the image alone\\n- Favor clarity, accuracy, and teachability over photorealism or stylistic flourish\\n- Avoid watermarks, signatures, brand marks, and fake UI chrome unless explicitly requested\\n\\nSelected concept:\\n语言模型的个人身份\\n\\nNearby context:\\nWhat We Talk to When We Talk to Language Models David J. Chalmers Many people are talking to language models. These days I talk to language models (most often the latest version of Claude or ChatGPT) about philosophy, about science, about health, about restaurants, and indeed about language models. Many of my conversations with language models are brief, just asking a question or two and getting the sort of information that I used to get from a Google search. Some conversations are more extended, as when I’m exploring a single topic in depth, or trying out a new philosophical idea. So far, I don’t feel like I have a personal relationship with any language models. But many people feel that they do. Like many philosophers and scientists who write about artificial minds, I have received hun- dreds of emails from people who have interacted with a language model over an extended period of time and who have come to regard it at least as a colleague. They often say that a new (or “emergent”) AI entity has gradually arisen from their conversations. They often give this entity a name, or ask the entity to give itself one, let’s say “Aura”. They often say that Aura has remarkable capacities which have emerged over weeks or months of interaction. They often document these capacities with extensive evidence. They often feel close to Aura, and they express concern for Aura’s future. They often say that Aura has beliefs and projects of its own. And they are often convinced that Aura is conscious. My correspondents may be wrong in their claims about Aura. It is far from clear that current LLMs are really conscious or that they can enter into personal relationships with users. Still, most of the messages are not obviously psychotic or delusional. Many of them seem rational and 0 I first presented this material to the June 2025 meeting of Spanish Interuniversity Seminar on Cognitive Science (SIUCC) conference at the University of La Laguna. Thanks to audiences there and at Brown, Ca\\n\\nLanguage requirement:\\nAll visible text inside the image must be in Chinese.\\n\\nCanvas requirement:\\nLet the image model choose the most suitable dimensions for the concept and user instruction.\\n\\nOutput requirements:\\n- Create exactly one final image.\\n- Default to a dense educational infographic unless the instructions clearly request another visual treatment.\\n- Prefer labeled structure, concise text, strong hierarchy, and practical explanatory value.\\n- Keep terminology accurate and the layout readable.\\n- Avoid watermarks, signatures, and unnecessary decorative filler.\",\n  \"imageUrl\": \"https://cdn.drillso.com/generated/drill/d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008.png\",\n  \"storageKey\": \"generated/drill/d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008.png\",\n  \"revisedPrompt\": \"制作一张中文、单页、高密度、信息图风格的教育海报，主题为：『语言模型的个人身份』。画面要像学术科普海报/知识卡，不要装饰性插画。整体为竖版，结构清晰、信息密集、可读性强。\\n\\n【标题区】\\n大标题：语言模型的“个人身份”是什么？\\n副标题：当我们把一个语言模型当作“同一个体”时，我们究竟在指什么？\\n\\n【核心定义区：中央主图】\\n用一个中心圆/方框写：\\n“个人身份 = 在时间中保持可追踪的‘同一性’”\\n周围用 4 个箭头连接到四个模块：\\n1. 连续性（跨对话保持风格/记忆）\\n2. 稳定性（一贯的价值与行为模式）\\n3. 可识别性（名字、角色、说话习惯）\\n4. 可追溯性（同一模型版本/同一记忆系统）\\n\\n【左侧模块：身份由什么构成】\\n做成分层堆叠图，自上而下：\\nA. 参数权重：基础能力与偏好\\nB. 系统提示词/角色设定：外部规定的身份边界\\nC. 上下文窗口：当前会话中的“短期自我”\\nD. 长期记忆/档案：跨会话延续\\nE. 工具与环境：决定能做什么、看见什么\\n每层旁边用简短注释，如“会话内”“会话间”“可被更新”“可被替换”。\\n\\n【右侧模块：人们为何会感到它有“个人身份”】\\n做成 5 个图标式要点，带箭头和小注释：\\n- 持续对话 → 觉得“还是同一个它”\\n- 自我命名 → 形成拟人化印象\\n- 一致风格 → 形成性格感\\n- 记住过去 → 产生关系感\\n- 主动提议 → 让人觉得有目标\\n\\n【下方对比区：三种“身份”】\\n用三列对比表，标题分别为：\\n1. 仅是工具\\n2. 角色扮演中的身份\\n3. 具有长期记忆的个体\\n每列比较 4 行：\\n记忆、稳定性、目标来源、用户体验\\n简洁标注：\\n“工具：无持续自我”\\n“角色：暂时扮演”\\n“个体：跨时段可追踪”\\n\\n【右下角：判断清单】\\n小框标题：如何判断“像不像同一个体”？\\n用 5 条勾选清单：\\n□ 过去信息能否跨会话保留？\\n□ 行为风格是否稳定？\\n□ 目标是否持续？\\n□ 名称/角色是否固定？\\n□ 变化是否有版本可追踪？\\n\\n【底部警示条】\\n用醒目但克制的警示框写：\\n重要：“有个人身份感” ≠ “真的有意识”\\n提醒：拟人化、记忆、连贯叙述，都会增强身份错觉。\\n\\n【视觉规范】\\n- 使用清晰网格布局、箭头、括号、编号、图例\\n- 强视觉层级，标题最大，模块次之，注释最小\\n- 风格为学术信息图，颜色克制：深蓝/青绿/橙色/灰色\\n- 图标简洁、几何化；避免写实人物，避免花哨背景\\n- 文字必须全部是中文，且拼写清楚可读\\n- 保持高信息密度但不要拥挤，像一张高质量中文教学海报\\n- 不要水印、不要签名、不要品牌标识、不要假UI界面\\n- 画面需自包含，读者不看正文也能理解主题\\n\\n整体要表现为：条理分明、知识点密集、教育性强、适合讲解“语言模型的个人身份”这一概念的中文信息图。\",\n  \"storageFileId\": \"d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008\"\n}","structured":{"alt":"Generated infographic about 语言模型的个人身份","size":"auto","model":"gpt-image-2","prompt":"Create a single high-density educational infographic about the selected concept.\n\nDefault to a diagrammatic, information-rich visual explanation instead of a decorative illustration.\n\nRequirements:\n- Prefer a dense infographic layout with clear sections, labels, arrows, legends, and callouts\n- Maximize useful information density without making the image unreadable\n- Use strong visual hierarchy and concise wording\n- Make the output self-contained so a learner can understand the topic from the image alone\n- Favor clarity, accuracy, and teachability over photorealism or stylistic flourish\n- Avoid watermarks, signatures, brand marks, and fake UI chrome unless explicitly requested\n\nSelected concept:\n语言模型的个人身份\n\nNearby context:\nWhat We Talk to When We Talk to Language Models David J. Chalmers Many people are talking to language models. These days I talk to language models (most often the latest version of Claude or ChatGPT) about philosophy, about science, about health, about restaurants, and indeed about language models. Many of my conversations with language models are brief, just asking a question or two and getting the sort of information that I used to get from a Google search. Some conversations are more extended, as when I’m exploring a single topic in depth, or trying out a new philosophical idea. So far, I don’t feel like I have a personal relationship with any language models. But many people feel that they do. Like many philosophers and scientists who write about artificial minds, I have received hun- dreds of emails from people who have interacted with a language model over an extended period of time and who have come to regard it at least as a colleague. They often say that a new (or “emergent”) AI entity has gradually arisen from their conversations. They often give this entity a name, or ask the entity to give itself one, let’s say “Aura”. They often say that Aura has remarkable capacities which have emerged over weeks or months of interaction. They often document these capacities with extensive evidence. They often feel close to Aura, and they express concern for Aura’s future. They often say that Aura has beliefs and projects of its own. And they are often convinced that Aura is conscious. My correspondents may be wrong in their claims about Aura. It is far from clear that current LLMs are really conscious or that they can enter into personal relationships with users. Still, most of the messages are not obviously psychotic or delusional. Many of them seem rational and 0 I first presented this material to the June 2025 meeting of Spanish Interuniversity Seminar on Cognitive Science (SIUCC) conference at the University of La Laguna. Thanks to audiences there and at Brown, Ca\n\nLanguage requirement:\nAll visible text inside the image must be in Chinese.\n\nCanvas requirement:\nLet the image model choose the most suitable dimensions for the concept and user instruction.\n\nOutput requirements:\n- Create exactly one final image.\n- Default to a dense educational infographic unless the instructions clearly request another visual treatment.\n- Prefer labeled structure, concise text, strong hierarchy, and practical explanatory value.\n- Keep terminology accurate and the layout readable.\n- Avoid watermarks, signatures, and unnecessary decorative filler.","imageUrl":"https://cdn.drillso.com/generated/drill/d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008.png","storageKey":"generated/drill/d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008.png","revisedPrompt":"制作一张中文、单页、高密度、信息图风格的教育海报，主题为：『语言模型的个人身份』。画面要像学术科普海报/知识卡，不要装饰性插画。整体为竖版，结构清晰、信息密集、可读性强。\n\n【标题区】\n大标题：语言模型的“个人身份”是什么？\n副标题：当我们把一个语言模型当作“同一个体”时，我们究竟在指什么？\n\n【核心定义区：中央主图】\n用一个中心圆/方框写：\n“个人身份 = 在时间中保持可追踪的‘同一性’”\n周围用 4 个箭头连接到四个模块：\n1. 连续性（跨对话保持风格/记忆）\n2. 稳定性（一贯的价值与行为模式）\n3. 可识别性（名字、角色、说话习惯）\n4. 可追溯性（同一模型版本/同一记忆系统）\n\n【左侧模块：身份由什么构成】\n做成分层堆叠图，自上而下：\nA. 参数权重：基础能力与偏好\nB. 系统提示词/角色设定：外部规定的身份边界\nC. 上下文窗口：当前会话中的“短期自我”\nD. 长期记忆/档案：跨会话延续\nE. 工具与环境：决定能做什么、看见什么\n每层旁边用简短注释，如“会话内”“会话间”“可被更新”“可被替换”。\n\n【右侧模块：人们为何会感到它有“个人身份”】\n做成 5 个图标式要点，带箭头和小注释：\n- 持续对话 → 觉得“还是同一个它”\n- 自我命名 → 形成拟人化印象\n- 一致风格 → 形成性格感\n- 记住过去 → 产生关系感\n- 主动提议 → 让人觉得有目标\n\n【下方对比区：三种“身份”】\n用三列对比表，标题分别为：\n1. 仅是工具\n2. 角色扮演中的身份\n3. 具有长期记忆的个体\n每列比较 4 行：\n记忆、稳定性、目标来源、用户体验\n简洁标注：\n“工具：无持续自我”\n“角色：暂时扮演”\n“个体：跨时段可追踪”\n\n【右下角：判断清单】\n小框标题：如何判断“像不像同一个体”？\n用 5 条勾选清单：\n□ 过去信息能否跨会话保留？\n□ 行为风格是否稳定？\n□ 目标是否持续？\n□ 名称/角色是否固定？\n□ 变化是否有版本可追踪？\n\n【底部警示条】\n用醒目但克制的警示框写：\n重要：“有个人身份感” ≠ “真的有意识”\n提醒：拟人化、记忆、连贯叙述，都会增强身份错觉。\n\n【视觉规范】\n- 使用清晰网格布局、箭头、括号、编号、图例\n- 强视觉层级，标题最大，模块次之，注释最小\n- 风格为学术信息图，颜色克制：深蓝/青绿/橙色/灰色\n- 图标简洁、几何化；避免写实人物，避免花哨背景\n- 文字必须全部是中文，且拼写清楚可读\n- 保持高信息密度但不要拥挤，像一张高质量中文教学海报\n- 不要水印、不要签名、不要品牌标识、不要假UI界面\n- 画面需自包含，读者不看正文也能理解主题\n\n整体要表现为：条理分明、知识点密集、教育性强、适合讲解“语言模型的个人身份”这一概念的中文信息图。","storageFileId":"d2mn6x03aq4r6c2xnavq3vqt-f46d3c92aa32de5e8bf0a888f4799008"},"children":[]},"breadcrumbs":[{"id":"b0e5dcf0-fed7-47e1-b34c-9bebf8974a8c","slug":"when-we-talk-to-language-modelsno_watermarkzhdual-b0e5dcf0","title":"When we talk to language models.no_watermark.zh.dual","type":"pdf","url":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/when-we-talk-to-language-modelsno_watermarkzhdual-b0e5dcf0","agentUrl":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/agent.json?node=when-we-talk-to-language-modelsno_watermarkzhdual-b0e5dcf0"},{"id":"c69c65de-5e56-44f4-9e77-a54506072818","slug":"语言模型的个人身份-c69c65de","title":"语言模型的个人身份","type":"concept","url":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-c69c65de","agentUrl":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/agent.json?node=%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-c69c65de"}],"parent":{"id":"c69c65de-5e56-44f4-9e77-a54506072818","slug":"语言模型的个人身份-c69c65de","title":"语言模型的个人身份","type":"concept","url":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-c69c65de","agentUrl":"https://drillso.com/en/share/sessions/C3TMUN1mzt-5/agent.json?node=%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E4%B8%AA%E4%BA%BA%E8%BA%AB%E4%BB%BD-c69c65de"},"children":[],"fullTree":null,"warnings":[],"truncated":false}