123 lines
3.5 KiB
Markdown
123 lines
3.5 KiB
Markdown
# LoCoMo
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## 概述
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LoCoMo 用于评估两人在多轮会话对话中的超长期对话记忆能力。该适配器支持来自 `locomo10.json` 的官方问答任务。
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## 任务描述
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- **任务类型**：长上下文问答
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- **输入**：包含会话日期的多轮对话历史和一个问题
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- **输出**：简短的自由格式答案
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- **子集**：`qa`
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## 主要特性
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- 使用托管在 ModelScope 上的官方 LoCoMo QA 数据文件
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- 支持完整历史的长上下文提示和仅含证据的 oracle 提示
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- 在数据中存在时包含图像说明，但不会下载图像文件
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- 使用 LoCoMo 基于规则的 F1 / 对抗性拒绝评分机制，而非基于大语言模型（LLM）的评判器
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## 评估说明
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- 默认子集为 `qa`，且 `eval_mode=long_context`
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- 使用 `extra_params.eval_mode='oracle_context'` 进行仅含证据的上限评估
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- 此 QA 适配器不包含事件摘要、多模态对话生成和 RAG 检索任务
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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** | `locomo` |
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| **数据集ID** | [evalscope/locomo](https://modelscope.cn/datasets/evalscope/locomo/summary) |
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| **论文** | N/A |
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| **标签** | `LongContext`, `MultiTurn`, `QA` |
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| **指标** | `f1` |
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| **默认示例数** | 0-shot |
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| **评估分割** | `test` |
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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 | 1,986 |
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| 提示词长度（平均） | 94097.72 字符 |
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| 提示词长度（最小/最大） | 55178 / 111026 字符 |
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## 样例示例
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**子集**: `qa`
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```json
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{
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"input": [
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{
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"id": "b585f7b0",
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"content": "Below is a conversation between two people: Caroline and Melanie. The conversation takes place over multiple days and the date of each conversation is wriiten at the beginning of the conversation.\n\nDATE: 1:56 pm on 8 May, 2023\nCONVERSATION:\nC ... [TRUNCATED 74397 chars] ... m of a short phrase for the following question. Answer with exact words from the context whenever possible.\n\nQuestion: When did Caroline go to the LGBTQ support group? Use DATE of CONVERSATION to answer with an approximate date. Short answer:"
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}
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],
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"target": "7 May 2023",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"sample_id": "conv-26",
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"qa_index": 0,
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"category": 2,
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"category_name": "temporal",
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"raw_question": "When did Caroline go to the LGBTQ support group?",
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"answer": "7 May 2023",
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"adversarial_answer": null,
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"eval_mode": "long_context",
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"question": "When did Caroline go to the LGBTQ support group? Use DATE of CONVERSATION to answer with an approximate date.",
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"evidence": [
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"D1:3"
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]
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}
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}
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```
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## 提示模板
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*未定义提示模板。*
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## 额外参数
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| 参数 | 类型 | 默认值 | 描述 |
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|-----------|------|---------|-------------|
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| `eval_mode` | `str` | `long_context` | 评估模式：long_context 或 oracle_context。可选值：['long_context', 'oracle_context'] |
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## 使用方法
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### 使用 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 locomo \
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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=['locomo'],
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dataset_args={
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'locomo': {
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# extra_params: {} # 使用默认额外参数
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}
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},
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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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``` |