167 lines
4.4 KiB
Markdown
167 lines
4.4 KiB
Markdown
# DROP
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## Overview
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DROP (Discrete Reasoning Over Paragraphs) is a challenging reading comprehension benchmark that requires models to perform discrete reasoning operations over text passages. Unlike simple extractive QA, DROP questions require numerical reasoning, counting, and comparison operations.
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## Task Description
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- **Task Type**: Reading Comprehension with Discrete Reasoning
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- **Input**: Passage and question requiring reasoning
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- **Output**: Numerical answer, span, or date
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- **Reasoning Types**: Addition, subtraction, counting, comparison, sorting
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## Key Features
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- 96,567 questions requiring discrete reasoning over text
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- Questions based on NFL game summaries, Wikipedia articles, etc.
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- Requires multi-step reasoning and arithmetic operations
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- Multiple valid answer formats (numbers, spans, dates)
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- Tests compositional reasoning abilities
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## Evaluation Notes
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- Default configuration uses **3-shot** examples
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- Metrics include Exact Match (EM) and token-level F1 score
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- Answers should follow the format: "Answer: [ANSWER]"
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- F1 score is the primary metric for comparison
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- Validates answers against multiple reference answers
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `drop` |
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| **Dataset ID** | [AI-ModelScope/DROP](https://modelscope.cn/datasets/AI-ModelScope/DROP/summary) |
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| **Paper** | N/A |
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| **Tags** | `Reasoning` |
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| **Metrics** | `em`, `f1` |
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| **Default Shots** | 3-shot |
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| **Evaluation Split** | `validation` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 9,536 |
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| Prompt Length (Mean) | 5454.05 chars |
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| Prompt Length (Min/Max) | 4638 / 9893 chars |
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## Sample Example
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**Subset**: `default`
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```json
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{
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"input": [
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{
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"id": "d4ab7ff6",
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"content": "You will be asked to read a passage and answer a question. Some examples of passages and Q&A are provided below.\n\n# Examples\n---\nPassage: Trunajaya rebellion or Trunajaya War was the ultimately unsuccessful rebellion waged by the Madurese pr ... [TRUNCATED] ... iled a 40-yard field goal, yet the Raiders' defense would shut down any possible attempt.\nQuestion: Who scored the first touchdown of the game?\n\nThink step by step, then write a line of the form \"Answer: [ANSWER]\" at the end of your response."
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}
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],
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"target": "[('Chaz Schilens',), ('JaMarcus Russell',)]",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"passage": " Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pa ... [TRUNCATED] ... 29-yard touchdown pass from Russell, followed up by an 80-yard punt return for a touchdown. The Texans tried to rally in the fourth quarter as Brown nailed a 40-yard field goal, yet the Raiders' defense would shut down any possible attempt.",
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"answer": {
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"number": "",
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"date": {
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"day": "",
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"month": "",
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"year": ""
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},
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"spans": [
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"Chaz Schilens"
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],
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"worker_id": "",
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"hit_id": ""
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},
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"validated_answers": {
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"number": [
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"",
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""
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],
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"date": [
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{
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"day": "",
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"month": "",
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"year": ""
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},
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{
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"day": "",
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"month": "",
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"year": ""
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}
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],
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"spans": [
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[
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"Chaz Schilens"
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],
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[
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"JaMarcus Russell"
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]
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],
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"worker_id": [
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"",
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""
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],
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"hit_id": [
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"",
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""
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]
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}
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}
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}
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```
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*Note: Some content was truncated for display.*
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## Prompt Template
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**Prompt Template:**
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```text
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You will be asked to read a passage and answer a question. {drop_examples}
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# Your Task
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---
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{query}
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Think step by step, then write a line of the form "Answer: [ANSWER]" at the end of your response.
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```
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## Usage
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### Using 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 drop \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using 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=['drop'],
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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