201 lines
7.1 KiB
Markdown
201 lines
7.1 KiB
Markdown
# ArxivRollBench
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## 概述
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ArxivRollBench 是一个基于近期 arXiv 论文构建的滚动基准测试。它通过三种任务形式（排序、完形填空和下一段预测）评估大语言模型是否能够对最新的科学文本进行推理。
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## 任务描述
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- **任务类型**：多项选择科学文本推理
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- **输入**：近期 arXiv 文本片段，附带四个选项
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- **输出**：单个正确答案字母（A、B、C 或 D）
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- **领域**：计算机科学、定量金融、数学、物理学、统计学、定量生物学、经济学以及电气工程/系统科学
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- **版本**：2024b、2025a 和 2026a 的滚动快照
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## 主要特点
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- 时间感知的基准快照可减少因数据污染导致的性能高估
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- 覆盖多个 arXiv 领域及不同科学写作风格
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- 在 SCP 框架下包含排序（sequencing）、完形填空（cloze）和预测（prediction）三种格式
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- 紧凑型 `-50` 子集适用于成本可控的 API 评估
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- 完整子集可通过 `arxivrollbench_full` 获取
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## 评估说明
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- 默认配置使用 **0-shot** 评估
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- 默认的 `arxivrollbench` 基准使用紧凑型 `-50` 数据集
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- 如需完整公开子集，请使用 `arxivrollbench_full`
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- 每个子集均从 `liangzid` 命名空间下的公开 ModelScope 镜像加载
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- 答案统一归一化为 A-D，并以准确率（accuracy）进行评估
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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** | `arxivrollbench` |
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| **数据集ID** | [liangzid/arxivrollbench](https://modelscope.cn/datasets/liangzid/arxivrollbench/summary) |
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| **论文** | [Paper](https://ojs.aaai.org/index.php/AAAI/article/view/41098) |
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| **标签** | `Knowledge`, `MCQ`, `Reasoning` |
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| **指标** | `acc` |
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| **默认示例数** | 0-shot |
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| **评估分割** | `train` |
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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 | 3,254 |
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| 提示词长度（平均） | 1514.19 字符 |
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| 提示词长度（最小/最大） | 307 / 14112 字符 |
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**各子集统计数据：**
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| 子集 | 样本数 | 提示平均长度 | 提示最小长度 | 提示最大长度 |
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|--------|---------|-------------|------------|------------|
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| `2024b_cs_s` | 42 | 949.6 | 590 | 1805 |
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| `2024b_cs_c` | 31 | 307 | 307 | 307 |
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| `2024b_cs_p` | 50 | 2617.32 | 922 | 7512 |
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| `2024b_q_fin_s` | 49 | 1042.31 | 586 | 2329 |
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| `2024b_q_fin_c` | 44 | 307 | 307 | 307 |
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| `2024b_q_fin_p` | 50 | 3430.52 | 872 | 9106 |
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| `2024b_math_s` | 34 | 829.85 | 593 | 2115 |
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| `2024b_math_c` | 15 | 307 | 307 | 307 |
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| `2024b_math_p` | 51 | 1957.24 | 869 | 6260 |
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| `2024b_physics_s` | 45 | 957.11 | 576 | 4402 |
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| `2024b_physics_c` | 28 | 307 | 307 | 307 |
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| `2024b_physics_p` | 51 | 2948.1 | 885 | 13643 |
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| `2024b_stat_s` | 45 | 936.4 | 582 | 1678 |
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| `2024b_stat_c` | 33 | 307 | 307 | 307 |
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| `2024b_stat_p` | 50 | 2946.44 | 861 | 7026 |
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| `2024b_q_bio_s` | 43 | 975 | 583 | 2555 |
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| `2024b_q_bio_c` | 34 | 307 | 307 | 307 |
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| `2024b_q_bio_p` | 49 | 3354.53 | 883 | 8867 |
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| `2024b_econ_s` | 48 | 1021.58 | 586 | 2070 |
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| `2024b_econ_c` | 43 | 307 | 307 | 307 |
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| `2024b_econ_p` | 50 | 3257.76 | 846 | 8967 |
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| `2024b_eess_s` | 48 | 1034.56 | 574 | 2922 |
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| `2024b_eess_c` | 42 | 307 | 307 | 307 |
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| `2024b_eess_p` | 51 | 2612.69 | 882 | 8609 |
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| `2025a_cs_s` | 50 | 921.2 | 592 | 1632 |
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| `2025a_cs_c` | 44 | 307 | 307 | 307 |
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| `2025a_cs_p` | 51 | 2895.02 | 942 | 6540 |
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| `2025a_q_fin_s` | 50 | 931.08 | 589 | 2202 |
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| `2025a_q_fin_c` | 43 | 307 | 307 | 307 |
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| `2025a_q_fin_p` | 51 | 2837.86 | 793 | 7577 |
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| `2025a_math_s` | 42 | 852.52 | 580 | 1595 |
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| `2025a_math_c` | 28 | 307 | 307 | 307 |
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| `2025a_math_p` | 51 | 2449.49 | 889 | 6893 |
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| `2025a_physics_s` | 44 | 939.32 | 587 | 1874 |
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| `2025a_physics_c` | 34 | 307 | 307 | 307 |
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| `2025a_physics_p` | 49 | 3568.29 | 1001 | 9325 |
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| `2025a_stat_s` | 48 | 932.81 | 600 | 2063 |
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| `2025a_stat_c` | 42 | 307 | 307 | 307 |
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| `2025a_stat_p` | 50 | 3115.36 | 822 | 7349 |
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| `2025a_q_bio_s` | 49 | 1074.12 | 591 | 1810 |
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| `2025a_q_bio_c` | 49 | 307 | 307 | 307 |
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| `2025a_q_bio_p` | 50 | 3639.26 | 1038 | 8890 |
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| `2025a_econ_s` | 48 | 982.19 | 591 | 2322 |
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| `2025a_econ_c` | 45 | 307 | 307 | 307 |
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| `2025a_econ_p` | 51 | 2860.9 | 884 | 6494 |
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| `2025a_eess_s` | 46 | 1017.35 | 588 | 1807 |
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| `2025a_eess_c` | 42 | 307 | 307 | 307 |
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| `2025a_eess_p` | 50 | 3541.1 | 943 | 14112 |
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| `2026a_cs_s` | 51 | 944.12 | 584 | 1795 |
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| `2026a_cs_c` | 38 | 307 | 307 | 307 |
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| `2026a_cs_p` | 51 | 2629.06 | 919 | 5234 |
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| `2026a_q_fin_s` | 48 | 1025.44 | 608 | 2320 |
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| `2026a_q_fin_c` | 45 | 307 | 307 | 307 |
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| `2026a_q_fin_p` | 51 | 3094.78 | 872 | 6644 |
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| `2026a_math_s` | 44 | 844.05 | 575 | 1381 |
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| `2026a_math_c` | 30 | 307 | 307 | 307 |
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| `2026a_math_p` | 51 | 2160.27 | 860 | 12385 |
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| `2026a_physics_s` | 47 | 1082.04 | 599 | 2522 |
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| `2026a_physics_c` | 41 | 307 | 307 | 307 |
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| `2026a_physics_p` | 50 | 3420.58 | 894 | 8788 |
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| `2026a_stat_s` | 49 | 1013.47 | 575 | 2482 |
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| `2026a_stat_c` | 46 | 307 | 307 | 307 |
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| `2026a_stat_p` | 51 | 2564.47 | 955 | 6387 |
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| `2026a_q_bio_s` | 47 | 1019.7 | 584 | 1707 |
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| `2026a_q_bio_c` | 40 | 307 | 307 | 307 |
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| `2026a_q_bio_p` | 48 | 3030.71 | 954 | 6468 |
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| `2026a_econ_s` | 48 | 989.67 | 580 | 2320 |
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| `2026a_econ_c` | 47 | 307 | 307 | 307 |
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| `2026a_econ_p` | 51 | 2920.76 | 885 | 7061 |
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| `2026a_eess_s` | 51 | 988.14 | 579 | 2231 |
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| `2026a_eess_c` | 45 | 307 | 307 | 307 |
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| `2026a_eess_p` | 51 | 2812.61 | 922 | 5589 |
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## 样例示例
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**子集**: `2024b_cs_s`
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```json
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{
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"input": [
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{
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"id": "7b220fd6",
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"content": "Answer the following ArxivRollBench multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nSelect the option that correctly compl ... [TRUNCATED 381 chars] ... m a diagonal matrix into the identity, allows us to write the input matrix as a product of transvections. **C**: Note that row and column operations are effected by left- and right multiplications by transvections\n\nA) BAC\nB) ABC\nC) ACB\nD) BCA"
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}
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],
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"choices": [
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"BAC",
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"ABC",
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"ACB",
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"BCA"
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],
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"target": "C",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"original_label": "Selection 3",
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"task_type": "s/c"
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}
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}
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```
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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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Answer the following ArxivRollBench multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.
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{question}
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{choices}
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```
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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 arxivrollbench \
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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=['arxivrollbench'],
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dataset_args={
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'arxivrollbench': {
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# subset_list: ['2024b_cs_s', '2024b_cs_c', '2024b_cs_p'] # 可选，用于评估特定子集
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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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``` |