sora 13274243a0 Bump vendored EvalScope and add K3-ready DPV4 configs.
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>
2026-09-02 07:30:48 +00:00

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_list to 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)