人工智能体共情能力模块设计与实践（下）

八、实验设计方案

8.1 数据集设计

建议构建一个多场景中文共情对话数据集。

场景分类

8.2 标注字段

每条样本建议标注：

{

\”user_text\”: \”我改了一晚上还是不行，真的受不了了\”,
\”emotion\”: \”frustration\”,
\”emotion_intensity\”: 0.86,
\”intent\”: \”task_help\”,
\”explicit_need\”: \”解决问题\”,
\”implicit_need\”: \”获得安抚和鼓励\”,
\”risk_level\”: \”low\”,
\”best_strategies\”: [\”情绪确认\”, \”处境复述\”, \”行动建议\”],
\”bad_response_example\”: \”请检查配置。\”,
\”good_response_example\”: \”这确实会让人很挫败，尤其是你已经花了一晚上。我们先别继续盲改，把报错信息和最近改动列出来，我帮你一步步定位。\”
}

8.3 对比实验

可以设计 4 组模型进行比较：

推荐目标是证明：

完整架构 Model D
在共情恰当性、任务帮助度、安全性、用户满意度上优于其他方法

8.4 A/B 测试指标

线上可以使用：

九、可落地的服务架构

9.1 微服务划分

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Client/API Gateway

Conversation Service

Context Service

Emotion Service

Intent Service

Risk Service

Strategy Service

User Profile Store

Conversation Memory Store

Model Serving

Policy Engine

LLM Response Service

Quality Checker

Safety Guardrail

Final Response

Logging Service

Training Data Pipeline

9.2 服务说明

十、工程落地优先级

MVP 版本

先做最小可用版本：

文本输入
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情绪识别
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意图识别
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大模型生成回复
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安全关键词兜底

MVP 架构

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