127 lines
3.7 KiB
Markdown
127 lines
3.7 KiB
Markdown
# EQ-Bench
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## 概述
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EQ-Bench 是一个用于评估语言模型在情感智能任务上表现的基准测试。它通过评估模型对对话中角色可能产生的情感反应强度进行打分，来衡量其情感理解能力。
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## 任务描述
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- **任务类型**：情感智能评估
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- **输入**：包含角色的对话场景
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- **输出**：特定格式的情感强度评分
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- **领域**：情感理解、社会认知
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## 主要特点
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- 测试模型预测对话中情感反应的能力
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- 要求对多种可能情绪的强度进行评分
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- 使用官方 EQ-Bench v2 评分算法
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- 评分包含 Sigmoid 缩放以处理微小差异
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- 引入调整常数，确保随机回答得分为 0
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## 评估说明
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- 默认评估使用 **validation**（验证）数据集划分
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- 主要指标：**EQ-Bench Score**（0-100 分制，报告为 0-1 范围）
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- 采用零样本（zero-shot）评估方式（不提供少样本示例）
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- 模型响应必须包含特定 JSON-like 格式的情感评分
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- 官方算法来源：[论文](https://arxiv.org/abs/2312.06281) | [官网](https://eqbench.com/)
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `eq_bench` |
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| **数据集 ID** | [evalscope/EQ-Bench](https://modelscope.cn/datasets/evalscope/EQ-Bench/summary) |
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| **论文** | N/A |
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| **标签** | `InstructionFollowing` |
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| **指标** | `eq_bench_score` |
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| **默认样本数** | 0-shot |
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| **评估数据划分** | `validation` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 171 |
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| 提示词长度（平均） | 1550.02 字符 |
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| 提示词长度（最小/最大） | 922 / 3737 字符 |
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## 样例示例
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**子集**: `default`
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```json
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{
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"input": [
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{
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"id": "97129bc9",
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"content": "Your task is to predict the likely emotional responses of a character in this dialogue:\n\nRobert: Claudia, you've always been the idealist. But let's be practical for once, shall we?\nClaudia: Practicality, according to you, means bulldozing ev ... [TRUNCATED] ... ary:\n\nRemorseful: <score>\nIndifferent: <score>\nAffectionate: <score>\nAnnoyed: <score>\n\n\n[End of answer]\n\nRemember: zero is a valid score, meaning they are likely not feeling that emotion. You must score at least one emotion > 0.\n\nYour answer:"
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}
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],
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"target": "{'emotion1': 'Remorseful', 'emotion2': 'Indifferent', 'emotion3': 'Affectionate', 'emotion4': 'Annoyed', 'emotion1_score': 2, 'emotion2_score': 3, 'emotion3_score': 0, 'emotion4_score': 5}",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"reference_answer": {
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"emotion1": "Remorseful",
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"emotion2": "Indifferent",
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"emotion3": "Affectionate",
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"emotion4": "Annoyed",
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"emotion1_score": 2,
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"emotion2_score": 3,
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"emotion3_score": 0,
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"emotion4_score": 5
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},
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"reference_answer_fullscale": {
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"emotion1": "Remorseful",
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"emotion2": "Indifferent",
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"emotion3": "Affectionate",
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"emotion4": "Annoyed",
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"emotion1_score": 0,
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"emotion2_score": "6",
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"emotion3_score": 0,
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"emotion4_score": "7"
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}
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}
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}
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```
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*注：部分内容因展示需要已被截断。*
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## 提示模板
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**提示模板：**
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```text
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{question}
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```
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## 使用方法
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### 使用命令行（CLI）
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets eq_bench \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['eq_bench'],
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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``` |