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>
4.4 KiB
4.4 KiB
ARC-Challenge-Indic
Overview
ARC-Challenge-Indic is a translation of the AI2 Reasoning Challenge (ARC-Challenge) science question-answering benchmark into 10 Indic languages, plus the original English set, for evaluating multilingual scientific reasoning.
Task Description
- Task Type: Multilingual Multiple-Choice Science Question Answering
- Input: Science question with answer choices in one of 11 languages
- Output: Correct answer letter
- Languages: Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu
Evaluation Notes
- Default configuration uses 0-shot evaluation (test split)
- Use
subset_listto evaluate specific languages (e.g.,['hi', 'ta']) - Same underlying science-exam questions as
arc(Challenge split), machine/human translated per language
Properties
| Property | Value |
|---|---|
| Benchmark Name | arc_indic |
| Dataset ID | sarvamai/arc-challenge-indic |
| Paper | N/A |
| Tags | MCQ, MultiLingual, Reasoning |
| Metrics | accuracy |
| Default Shots | 0-shot |
| Evaluation Split | test |
| Train Split | validation |
Data Statistics
| Metric | Value |
|---|---|
| Total Samples | 12,647 |
| Prompt Length (Mean) | 448.01 chars |
| Prompt Length (Min/Max) | 236 / 2053 chars |
Per-Subset Statistics:
| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
|---|---|---|---|---|
bn |
1,150 | 432.51 | 242 | 1137 |
en |
1,147 | 454.88 | 253 | 1111 |
gu |
1,150 | 426.57 | 243 | 1098 |
hi |
1,150 | 443.47 | 236 | 1162 |
kn |
1,150 | 456.08 | 245 | 1199 |
ml |
1,150 | 473.31 | 239 | 2053 |
mr |
1,150 | 434.22 | 242 | 1133 |
or |
1,150 | 440.04 | 243 | 1374 |
pa |
1,150 | 443.35 | 236 | 1132 |
ta |
1,150 | 479.12 | 243 | 1295 |
te |
1,150 | 444.53 | 244 | 1172 |
Sample Example
Subset: bn
{
"input": [
{
"id": "f750462b",
"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\nএকজন খগোলবিদ পর্যবেক্ষণ করেন যে একটি উল্কা পতনের পরে একটি গ্রহের ঘূর্ণন গতি বেড়ে যায়। ঘূর্ণন বৃদ্ধির ফলে কোন প্রভাবটি সবচেয়ে বেশি সম্ভাব্য?\n\nA) গ্রহের ঘনত্ব কমে যাবে।\nB) গ্রহীয় বছরগুলি আরও দীর্ঘ হবে।\nC) গ্রহের দিনগুলি ছোট হয়ে যাবে।\nD) গ্রহের মাধ্যাকর্ষণ শক্তি আরও বৃদ্ধি পাবে।"
}
],
"choices": [
"গ্রহের ঘনত্ব কমে যাবে।",
"গ্রহীয় বছরগুলি আরও দীর্ঘ হবে।",
"গ্রহের দিনগুলি ছোট হয়ে যাবে।",
"গ্রহের মাধ্যাকর্ষণ শক্তি আরও বৃদ্ধি পাবে।"
],
"target": "C",
"id": 0,
"group_id": 0,
"metadata": {
"id": "Mercury_7175875",
"language": "Bengali"
}
}
Prompt Template
Prompt Template:
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}.
{question}
{choices}
Usage
Using CLI
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets arc_indic \
--limit 10 # Remove this line for formal evaluation
Using 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=['arc_indic'],
dataset_args={
'arc_indic': {
# subset_list: ['bn', 'en', 'gu'] # optional, evaluate specific subsets
}
},
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)