13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
146 lines
4.6 KiB
Markdown
146 lines
4.6 KiB
Markdown
# IndicParam
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## Overview
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IndicParam is a graduate-level benchmark evaluating LLM understanding of low- and extremely
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low-resource Indic languages. All 13,207 multiple-choice questions are sourced from official UGC-NET
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language question papers and answer keys, presented in each language's native script (or code-mixed
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form for Sanskrit-English).
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## Task Description
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- **Task Type**: Graduate-Level Multiple-Choice Question Answering
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- **Input**: A UGC-NET exam question with 4 answer choices, in a low-resource Indic language
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- **Output**: Correct answer letter
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- **Languages**: Bodo, Dogri, Gujarati (Surya script), Konkani, Maithili, Marathi, Nepali, Oriya,
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Rajasthani, Sanskrit, Sanskrit-English code-mixed, Santali
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## Key Features
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- 13,207 multiple-choice questions sourced from official UGC-NET language question papers
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- 12 low-resource Indic languages/scripts, including extremely low-resource ones like Bodo and Santali
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- Questions are presented in each language's native script (or code-mixed form for Sanskrit-English)
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- All languages ship in a single dataset config, differentiated by the `subject` field
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation (test split, the only split available)
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- Use `subset_list` to evaluate specific languages
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- All languages ship in a single dataset config, differentiated by the `subject` field; this adapter
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reformats by that field
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `indic_param` |
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| **Dataset ID** | [bharatgenai/IndicParam](https://modelscope.cn/datasets/bharatgenai/IndicParam/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MCQ`, `MultiLingual` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 13,207 |
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| Prompt Length (Mean) | 376.02 chars |
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| Prompt Length (Min/Max) | 218 / 1413 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `Bodo` | 1,313 | 461.37 | 256 | 738 |
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| `Dogri` | 1,027 | 487.72 | 245 | 853 |
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| `Gujarati_surya` | 1,044 | 395.79 | 255 | 611 |
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| `Konkani` | 1,328 | 396.77 | 245 | 1413 |
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| `Maithili` | 1,286 | 284.67 | 218 | 451 |
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| `Marathi` | 1,245 | 382.66 | 242 | 957 |
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| `Nepali` | 1,038 | 406.12 | 260 | 857 |
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| `Oriya` | 577 | 365.04 | 239 | 924 |
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| `Rajasthani` | 1,190 | 321.32 | 237 | 1136 |
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| `Sanskrit` | 1,315 | 304.51 | 229 | 833 |
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| `Sanskrit Mix` | 971 | 352.41 | 253 | 693 |
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| `Santali` | 873 | 366.16 | 233 | 809 |
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## Sample Example
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**Subset**: `Bodo`
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```json
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{
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"input": [
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{
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"id": "0616580a",
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"content": "Answer the following 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\nआथिखालाव सुबुं थुनलाइफोरखौ बुथुमनो थाखाय बबे आदबखौ रासिनै बाहायनाय जायो\n\nA) फट' दैखांनाय\nB) रेकरडिं खालामनाय\nC) सल बुंहोनाय\nD) सल खोनासंनाय"
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}
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],
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"choices": [
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"फट' दैखांनाय",
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"रेकरडिं खालामनाय",
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"सल बुंहोनाय",
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"सल खोनासंनाय"
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],
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"target": "B",
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"id": 0,
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"group_id": 0,
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"subset_key": "Bodo",
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"metadata": {
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"subject": "Bodo",
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"exam_name": "Question Papers of NET Dec. 2012 Bodo Paper III hindi"
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}
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}
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```
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## Prompt Template
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**Prompt Template:**
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```text
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Answer the following 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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## 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 indic_param \
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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=['indic_param'],
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dataset_args={
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'indic_param': {
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# subset_list: ['Bodo', 'Dogri', 'Gujarati_surya'] # optional, evaluate specific subsets
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}
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},
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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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