BoolQ-Indic#

Overview#

BoolQ-Indic is a translation of the BoolQ yes/no reading-comprehension benchmark into 10 Indic languages plus English, for evaluating multilingual passage understanding.

Task Description#

Task Type: Multilingual Yes/No Reading Comprehension
Input: Passage + yes/no question in one of 11 languages
Output: Yes or No
Languages: Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu

Evaluation Notes#

Default configuration uses 0-shot evaluation (validation split)
Use subset_list to evaluate specific languages (e.g., ['hi', 'ta']), or limit to cap sample count — the full default run is 35,970 samples across all 11 languages
Set few_shot_num > 0 to enable few-shot prompting; examples are drawn from the train split
All languages ship in a single dataset config; this adapter reformats by the language field

Properties#

Property	Value
Benchmark Name	`indic_boolq`
Dataset ID	sarvamai/boolq-indic
Paper	N/A
Tags	`MCQ`, `MultiLingual`, `ReadingComprehension`
Metrics	`accuracy`
Default Shots	0-shot
Evaluation Split	`validation`
Train Split	`train`

Data Statistics#

Metric	Value
Total Samples	35,970
Prompt Length (Mean)	822.66 chars
Prompt Length (Min/Max)	275 / 5035 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`bn`	3,270	801.95	294	2308
`en`	3,270	814.26	292	5035
`gu`	3,270	793.37	283	2105
`hi`	3,270	818.47	297	3078
`kn`	3,270	833.99	275	2920
`ml`	3,270	869.99	294	3558
`mr`	3,270	806.68	289	2593
`or`	3,270	787.68	306	1482
`pa`	3,270	804.93	295	1975
`ta`	3,270	904.01	297	3570
`te`	3,270	813.95	284	3312

Sample Example#

Subset: bn

{
  "input": [
    {
      "id": "0f04f0f7",
      "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.\n\nসকল জৈববস্তুই কমপক্ষে এই ধাপগুলোর মধ্য দিয়ে যায়: এগুলো চা ... [TRUNCATED 1099 chars] ...  বার্কলেতে  ছয়টি পৃথক গবেষণা বিশ্লেষণ করার পর, একটি গবেষণায় উপসংহারে আসা গেছে যে, ভুট্টা থেকে ইথানল উৎপাদনে পেট্রোলিয়ামের ব্যবহার গ্যাসোলিন উৎপাদনের তুলনায় অনেক কম।\n\nQuestion: ইথানল উৎপাদনের চেয়ে  তৈরিতে কি বেশি শক্তি লাগে??\n\nA) Yes\nB) No"
    }
  ],
  "choices": [
    "Yes",
    "No"
  ],
  "target": "B",
  "id": 0,
  "group_id": 0,
  "subset_key": "bn",
  "metadata": {
    "language": "Bengali"
  }
}

Note: Some content was truncated for display.

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 indic_boolq \
    --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=['indic_boolq'],
    dataset_args={
        'indic_boolq': {
            # subset_list: ['bn', 'en', 'gu']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)