glm-5-turbo 在「段落润色改写」的评测结果
这是该 AI 模型在此测试用例上的详细评测结果。
基本信息
- 模型名称：glm-5-turbo
- 用例名称：段落润色改写
- 测试类型：xsct-a
- 评测维度：文档润色与优化
系统提示词（System Prompt）
这是给 AI 模型的背景设定和角色指令：
You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).
用户提示词（User Prompt）
这是用户给 AI 模型的具体任务要求：
以下是当前工作区中的文档 `project_report.docx` 摘要： **文档标题**：2024年第三季度项目进展报告 **作者**：产品运营部 **字数**：约 3200 字 **章节结构**： 1. 执行摘要 2. 项目背景与目标 3. Q3 主要里程碑完成情况 4. 资源投入与成本分析 5. 风险与问题记录 6. Q4 工作计划 7. 附件：数据支撑材料 其中「Q4 工作计划」章节的原文如下： --- Q4我们打算做这几件事： 1. 专业技能提升：让团队的人去学一些新技术，比如大模型、数据分析啥的，提高大家的能力水平。 2. 行业前瞻研究：多关注一下行业里的新动态，看看竞品都在搞什么，然后出几篇研究报告。 3. 跨部门协作：跟技术部、市场部多沟通沟通，把信息拉通，一起推进重点项目。 4. 流程优化：把现在一些不太顺畅的工作流程理一理，提高效率。 --- 请帮我把上述「Q4 工作计划」内容润色改写，要求：将所有要点合并成一个自然段落，不要使用列表或分条列项的格式；语言风格从口语化转为正式书面语，表达专业洗练；确保语意连贯、行文流畅，同时保留原文的所有核心信息。
任务要求
AI 模型需要满足以下要求：
- 输出必须是一个连贯的自然段落，不得出现编号列表、项目符号或分条列项格式
- 语言风格须为正式书面语，消除口语化表达（如"啥的"
- 搞什么
- 理一理
- 等）
- 原文四个要点（专业技能提升、行业前瞻研究、跨部门协作、流程优化）的核心信息均须保留
- 段落内部逻辑通顺，各要点之间有自然的衔接过渡，行文流畅
评测结果
综合得分
该模型在此用例的得分为：96.8 分（满分 100 分），已通过
基础难度评测结果
- 得分：96.8 分
- 通过状态：已通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
第四季度，团队将聚焦于四个维度的能力建设与协同提升。在专业技能方面，系统推进大模型应用、数据分析等前沿技术的学习与赋能，全面提升团队的专业素养与技术储备；在行业洞察方面，持续追踪行业发展趋势与竞品动态，形成若干份高质量的前瞻性研究报告，为战略决策提供参考依据；在组织协同方面，深化与技术部、市场部的跨部门沟通机制，推动信息互通与资源整合，合力保障重点项目的落地推进；在运营效能方面，对现有工作流程进行全面梳理与优化，消除堵点、提升效率，构建更加规范高效的业务运转体系。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出整体质量优秀，四要点信息完整保留，语言风格成功转为正式书面语，格式严格合规。行文采用「在……方面」的四段并列结构，逻辑清晰但衔接方式略显单一，稍逊于参考答案中更富变化的过渡表达。综合来看，这是一份高质量的润色改写结果。 【GEMINI】模型表现出色，完美达成了所有润色要求。不仅成功将口语化内容转化为正式书面语，还通过合理的逻辑结构将四个要点有机整合为一个连贯的自然段落，展现了极高的文本处理能力和专业素养。 【KIMI】候选输出是一份高质量的润色结果，完整保留了原文四个核心要点，成功将口语化表达转为专业书面语，格式严格符合单一自然段落要求，行文流畅且逻辑清晰。 minor不足在于排比结构略显规整，可进一步丰富衔接手法，但整体表现优秀，已达到商务正式文档的标准。
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