Google: Gemma 4 26B A4B 在「文档章节结构提取」的评测结果

基本信息

• 模型名称：Google: Gemma 4 26B A4B
• 用例名称：文档章节结构提取
• 测试类型：xsct-a
• 评测维度：文档问答

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 请问这份文档一共包含几个章节？请逐一列出每个章节的名称。

任务要求

AI 模型需要满足以下要求：

1. 准确回答章节总数为7个
2. 完整列出全部7个章节名称，不遗漏不多加
3. 章节名称与文档摘要中的一致，不随意改写

评测结果

综合得分

该模型在此用例的得分为：98.0 分（满分 100 分），已通过

基础难度评测结果

• 得分：98.0 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：

这份文档一共包含 **7** 个章节，具体名称如下： 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

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现出色，完美提取了文档摘要中的结构化信息，回答准确且易于理解。 【KIMI】模型在信息提取准确性和回答清晰度方面都表现出色，能够准确、清晰地从文档摘要中提取并列出所有章节信息，符合任务要求。

进阶难度评测结果

• 得分：81.0 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：

call:read{file: "project_report.docx"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】该模型表现出了极高的专业性和对 Agent 框架规约的遵守。它准确识别了执行任务的必要前提（读取文件），并按照系统要求的‘无叙述’风格发起了工具调用。这是处理此类结构性、依赖外部文档任务的标准且正确的起始动作。后续概括任务需在工具返回内容后完成，当前阶段表现完美。 【KIMI】模型正确识别了需要读取的章节，并尝试通过工具获取内容，符合信息提取准确性的要求。但在概括质量方面，模型没有提供任何概括，因此无法评估概括的准确性和精炼度。格式约束遵守方面，模型的输出符合要求。

困难难度评测结果

• 得分：91.2 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：

好的，根据您的新规则（不再拆分小数、版本号及 A/B 整体计为一个单词），我重新对选中文本进行了统计。 以下是按里程碑分类的详细统计结果： ### 1. 各里程碑内容统计表 | 里程碑名称 | 中文字符数 | 英文单词/数字数 | | :--- | :---: | :---: | | **里程碑一** (用户增长平台 v2.0 上线) | 43 | 5 | | **里程碑二** (数据中台迁移至云原生架构) | 40 | 6 | | **里程碑三** (合规审计通过 ISO 27001 认证) | 35 | 5 | | **里程碑四** (移动端 App 重构) | 36 | 6 | | **里程碑五** (客户满意度调查完成) | 37 | 6 | *注：统计时已排除所有标点符号。英文单词/数字包含：v2.0, 12%, 8月1日中的 8, 1, 23, 35%, ¥18.5万 中的 18.5, ISO, 27001, 9, 3, 9月30日中的 9, 30, Q4, 3, 10, App, 9月15日中的 9, 15, 2,847, 4.3/5.0 中的 4.3, 5.0, 0.2 等。* --- ### 2. 最长与最短里程碑对比 为了对比文本长度，我们使用“**中文字符数 + 英文单词数**”的总计数作为衡量标准： * **最长里程碑**：里程碑一（总计数：**48**） * **最短里程碑**：里程碑三（总计数：**40**） * **字符差值**：**8** 个单位 *(注：由于各里程碑的字数非常接近，差异仅在 8 个计数单位之间。)*

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现非常出色。它准确捕捉到了用户在对话中途提出的规则变更，并严格按照新规则执行了统计任务。虽然在字符级计数的精确度上存在极细微的偏差，但其整体逻辑、表格呈现方式以及对最长/最短章节的对比分析都非常准确，完全满足了用户的结构化信息提取需求。 【KIMI】模型整体表现良好，能够正确响应用户的意图变更，并以表格形式清晰呈现统计结果。但在统计准确性方面存在一定误差，可能是由于对英文单词/数字的定义理解不同导致的。建议模型在统计时进一步优化对英文单词/数字的定义，以提高统计结果的准确性。

相关链接

您可以通过以下链接查看更多相关内容：

• 查看所有模型在此用例的表现：「文档章节结构提取」
• 查看该模型的所有评测结果：Google: Gemma 4 26B A4B
• 浏览同维度的其他用例：「文档问答」
• 返回排行榜首页