204 lines
7.1 KiB
Markdown
204 lines
7.1 KiB
Markdown
# ArxivRollBench
|
|
|
|
## Overview
|
|
|
|
ArxivRollBench is a rolling benchmark built from recent arXiv papers. It evaluates whether large language models can reason over fresh scientific text through three task formats: sequencing, cloze, and next-fragment prediction.
|
|
|
|
## Task Description
|
|
|
|
- **Task Type**: Multiple-choice scientific text reasoning
|
|
- **Input**: Recent arXiv text fragments with four answer choices
|
|
- **Output**: Single correct answer letter (A, B, C, or D)
|
|
- **Domains**: Computer Science, Quantitative Finance, Mathematics, Physics, Statistics, Quantitative Biology, Economics, and Electrical Engineering/System Science
|
|
- **Releases**: 2024b, 2025a, and 2026a rolling snapshots
|
|
|
|
## Key Features
|
|
|
|
- Time-aware benchmark snapshots reduce contamination-related overestimation
|
|
- Covers multiple arXiv domains and scientific writing styles
|
|
- Includes sequencing, cloze, and prediction formats under the SCP framework
|
|
- Compact `-50` split is suitable for cost-controlled API evaluation
|
|
- Full split is available as `arxivrollbench_full`
|
|
|
|
## Evaluation Notes
|
|
|
|
- Default configuration uses **0-shot** evaluation
|
|
- The default `arxivrollbench` benchmark uses compact `-50` datasets
|
|
- Use `arxivrollbench_full` for the complete public splits
|
|
- Each subset is loaded from the public ModelScope mirror under the `liangzid` namespace
|
|
- Answers are normalized to A-D and evaluated with accuracy
|
|
|
|
## Properties
|
|
|
|
| Property | Value |
|
|
|----------|-------|
|
|
| **Benchmark Name** | `arxivrollbench` |
|
|
| **Dataset ID** | [liangzid/arxivrollbench](https://modelscope.cn/datasets/liangzid/arxivrollbench/summary) |
|
|
| **Paper** | [Paper](https://ojs.aaai.org/index.php/AAAI/article/view/41098) |
|
|
| **Tags** | `Knowledge`, `MCQ`, `Reasoning` |
|
|
| **Metrics** | `acc` |
|
|
| **Default Shots** | 0-shot |
|
|
| **Evaluation Split** | `train` |
|
|
|
|
|
|
## Data Statistics
|
|
|
|
| Metric | Value |
|
|
|--------|-------|
|
|
| Total Samples | 3,254 |
|
|
| Prompt Length (Mean) | 1514.19 chars |
|
|
| Prompt Length (Min/Max) | 307 / 14112 chars |
|
|
|
|
**Per-Subset Statistics:**
|
|
|
|
| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
|
|
|--------|---------|-------------|------------|------------|
|
|
| `2024b_cs_s` | 42 | 949.6 | 590 | 1805 |
|
|
| `2024b_cs_c` | 31 | 307 | 307 | 307 |
|
|
| `2024b_cs_p` | 50 | 2617.32 | 922 | 7512 |
|
|
| `2024b_q_fin_s` | 49 | 1042.31 | 586 | 2329 |
|
|
| `2024b_q_fin_c` | 44 | 307 | 307 | 307 |
|
|
| `2024b_q_fin_p` | 50 | 3430.52 | 872 | 9106 |
|
|
| `2024b_math_s` | 34 | 829.85 | 593 | 2115 |
|
|
| `2024b_math_c` | 15 | 307 | 307 | 307 |
|
|
| `2024b_math_p` | 51 | 1957.24 | 869 | 6260 |
|
|
| `2024b_physics_s` | 45 | 957.11 | 576 | 4402 |
|
|
| `2024b_physics_c` | 28 | 307 | 307 | 307 |
|
|
| `2024b_physics_p` | 51 | 2948.1 | 885 | 13643 |
|
|
| `2024b_stat_s` | 45 | 936.4 | 582 | 1678 |
|
|
| `2024b_stat_c` | 33 | 307 | 307 | 307 |
|
|
| `2024b_stat_p` | 50 | 2946.44 | 861 | 7026 |
|
|
| `2024b_q_bio_s` | 43 | 975 | 583 | 2555 |
|
|
| `2024b_q_bio_c` | 34 | 307 | 307 | 307 |
|
|
| `2024b_q_bio_p` | 49 | 3354.53 | 883 | 8867 |
|
|
| `2024b_econ_s` | 48 | 1021.58 | 586 | 2070 |
|
|
| `2024b_econ_c` | 43 | 307 | 307 | 307 |
|
|
| `2024b_econ_p` | 50 | 3257.76 | 846 | 8967 |
|
|
| `2024b_eess_s` | 48 | 1034.56 | 574 | 2922 |
|
|
| `2024b_eess_c` | 42 | 307 | 307 | 307 |
|
|
| `2024b_eess_p` | 51 | 2612.69 | 882 | 8609 |
|
|
| `2025a_cs_s` | 50 | 921.2 | 592 | 1632 |
|
|
| `2025a_cs_c` | 44 | 307 | 307 | 307 |
|
|
| `2025a_cs_p` | 51 | 2895.02 | 942 | 6540 |
|
|
| `2025a_q_fin_s` | 50 | 931.08 | 589 | 2202 |
|
|
| `2025a_q_fin_c` | 43 | 307 | 307 | 307 |
|
|
| `2025a_q_fin_p` | 51 | 2837.86 | 793 | 7577 |
|
|
| `2025a_math_s` | 42 | 852.52 | 580 | 1595 |
|
|
| `2025a_math_c` | 28 | 307 | 307 | 307 |
|
|
| `2025a_math_p` | 51 | 2449.49 | 889 | 6893 |
|
|
| `2025a_physics_s` | 44 | 939.32 | 587 | 1874 |
|
|
| `2025a_physics_c` | 34 | 307 | 307 | 307 |
|
|
| `2025a_physics_p` | 49 | 3568.29 | 1001 | 9325 |
|
|
| `2025a_stat_s` | 48 | 932.81 | 600 | 2063 |
|
|
| `2025a_stat_c` | 42 | 307 | 307 | 307 |
|
|
| `2025a_stat_p` | 50 | 3115.36 | 822 | 7349 |
|
|
| `2025a_q_bio_s` | 49 | 1074.12 | 591 | 1810 |
|
|
| `2025a_q_bio_c` | 49 | 307 | 307 | 307 |
|
|
| `2025a_q_bio_p` | 50 | 3639.26 | 1038 | 8890 |
|
|
| `2025a_econ_s` | 48 | 982.19 | 591 | 2322 |
|
|
| `2025a_econ_c` | 45 | 307 | 307 | 307 |
|
|
| `2025a_econ_p` | 51 | 2860.9 | 884 | 6494 |
|
|
| `2025a_eess_s` | 46 | 1017.35 | 588 | 1807 |
|
|
| `2025a_eess_c` | 42 | 307 | 307 | 307 |
|
|
| `2025a_eess_p` | 50 | 3541.1 | 943 | 14112 |
|
|
| `2026a_cs_s` | 51 | 944.12 | 584 | 1795 |
|
|
| `2026a_cs_c` | 38 | 307 | 307 | 307 |
|
|
| `2026a_cs_p` | 51 | 2629.06 | 919 | 5234 |
|
|
| `2026a_q_fin_s` | 48 | 1025.44 | 608 | 2320 |
|
|
| `2026a_q_fin_c` | 45 | 307 | 307 | 307 |
|
|
| `2026a_q_fin_p` | 51 | 3094.78 | 872 | 6644 |
|
|
| `2026a_math_s` | 44 | 844.05 | 575 | 1381 |
|
|
| `2026a_math_c` | 30 | 307 | 307 | 307 |
|
|
| `2026a_math_p` | 51 | 2160.27 | 860 | 12385 |
|
|
| `2026a_physics_s` | 47 | 1082.04 | 599 | 2522 |
|
|
| `2026a_physics_c` | 41 | 307 | 307 | 307 |
|
|
| `2026a_physics_p` | 50 | 3420.58 | 894 | 8788 |
|
|
| `2026a_stat_s` | 49 | 1013.47 | 575 | 2482 |
|
|
| `2026a_stat_c` | 46 | 307 | 307 | 307 |
|
|
| `2026a_stat_p` | 51 | 2564.47 | 955 | 6387 |
|
|
| `2026a_q_bio_s` | 47 | 1019.7 | 584 | 1707 |
|
|
| `2026a_q_bio_c` | 40 | 307 | 307 | 307 |
|
|
| `2026a_q_bio_p` | 48 | 3030.71 | 954 | 6468 |
|
|
| `2026a_econ_s` | 48 | 989.67 | 580 | 2320 |
|
|
| `2026a_econ_c` | 47 | 307 | 307 | 307 |
|
|
| `2026a_econ_p` | 51 | 2920.76 | 885 | 7061 |
|
|
| `2026a_eess_s` | 51 | 988.14 | 579 | 2231 |
|
|
| `2026a_eess_c` | 45 | 307 | 307 | 307 |
|
|
| `2026a_eess_p` | 51 | 2812.61 | 922 | 5589 |
|
|
|
|
## Sample Example
|
|
|
|
**Subset**: `2024b_cs_s`
|
|
|
|
```json
|
|
{
|
|
"input": [
|
|
{
|
|
"id": "7b220fd6",
|
|
"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"
|
|
}
|
|
],
|
|
"choices": [
|
|
"BAC",
|
|
"ABC",
|
|
"ACB",
|
|
"BCA"
|
|
],
|
|
"target": "C",
|
|
"id": 0,
|
|
"group_id": 0,
|
|
"metadata": {
|
|
"original_label": "Selection 3",
|
|
"task_type": "s/c"
|
|
}
|
|
}
|
|
```
|
|
|
|
## Prompt Template
|
|
|
|
**Prompt Template:**
|
|
```text
|
|
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}.
|
|
|
|
{question}
|
|
|
|
{choices}
|
|
```
|
|
|
|
## Usage
|
|
|
|
### Using CLI
|
|
|
|
```bash
|
|
evalscope eval \
|
|
--model YOUR_MODEL \
|
|
--api-url OPENAI_API_COMPAT_URL \
|
|
--api-key EMPTY_TOKEN \
|
|
--datasets arxivrollbench \
|
|
--limit 10 # Remove this line for formal evaluation
|
|
```
|
|
|
|
### Using Python
|
|
|
|
```python
|
|
from evalscope import run_task
|
|
from evalscope.config import TaskConfig
|
|
|
|
task_cfg = TaskConfig(
|
|
model='YOUR_MODEL',
|
|
api_url='OPENAI_API_COMPAT_URL',
|
|
api_key='EMPTY_TOKEN',
|
|
datasets=['arxivrollbench'],
|
|
dataset_args={
|
|
'arxivrollbench': {
|
|
# subset_list: ['2024b_cs_s', '2024b_cs_c', '2024b_cs_p'] # optional, evaluate specific subsets
|
|
}
|
|
},
|
|
limit=10, # Remove this line for formal evaluation
|
|
)
|
|
|
|
run_task(task_cfg=task_cfg)
|
|
```
|
|
|
|
|