BhashaBench-Multi (Krishi)#

Overview#

BhashaBench-Multi (Krishi) is a domain-specific multiple-choice benchmark evaluating LLM knowledge of agriculture (Krishi) across 22 Indic languages. Each question originates in English and is machine translated (with LLM-judged translation quality scores) into the target language; this adapter uses the translated question/choices.

Task Description#

Task Type: Domain-Specific Multiple-Choice Question Answering
Input: A agriculture (Krishi) question with 4 answer choices, in one of 22 Indic languages
Output: Correct answer letter
Languages: Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Santhali, Sindhi, Tamil, Telugu, Urdu

Key Features#

~14,963 questions per language across 22 Indic languages per domain (~330k total per domain)
Machine-translated from English with LLM-judged translation quality scores
22 scheduled languages of India, all in native script; no English split
Four domains available as separate benchmarks: Ayurveda, Finance, Krishi, Legal

Evaluation Notes#

Default configuration uses 0-shot evaluation (test split, the only split available)
Use subset_list to evaluate specific languages (e.g., ['Hindi', 'Tamil']), or limit to cap sample count — each domain is ~14,963 questions per language across 22 languages (~330k total), so evaluating every language’s full split is a large run
No English split exists for this dataset

Properties#

Property	Value
Benchmark Name	`bhasha_bench_multi_krishi`
Dataset ID	bharatgenai/BhashaBench-Multi
Paper	N/A
Tags	`Knowledge`, `MCQ`, `MultiLingual`
Metrics	`accuracy`
Default Shots	0-shot
Evaluation Split	`test`

Data Statistics#

Metric	Value
Total Samples	338,910
Prompt Length (Mean)	411.84 chars
Prompt Length (Min/Max)	207 / 2882 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`Assamese`	15,405	402.14	224	2265
`Bengali`	15,405	406.38	224	1506
`Bodo`	15,405	417.81	207	2186
`Dogri`	15,405	403.83	220	1988
`Gujarati`	15,405	397.6	224	1380
`Hindi`	15,405	407.8	224	1572
`Kannada`	15,405	407.21	224	1407
`Kashmiri`	15,405	442.73	245	2668
`Konkani`	15,405	402.57	222	1969
`Maithili`	15,405	393.7	224	1783
`Malayalam`	15,405	429.89	224	1661
`Manipuri`	15,405	436.33	240	2882
`Marathi`	15,405	406.47	224	1520
`Nepali`	15,405	402.05	224	1440
`Oriya`	15,405	392.2	221	1366
`Punjabi`	15,405	404.2	222	1536
`Sanskrit`	15,405	411.22	224	1412
`Santhali`	15,405	440.59	234	2773
`Sindhi`	15,405	392.36	224	1233
`Tamil`	15,405	441.53	224	2165
`Telugu`	15,405	412.75	224	1432
`Urdu`	15,405	409.12	224	2132

Sample Example#

Subset: Assamese

{
  "input": [
    {
      "id": "70df7242",
      "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\nইয়াকোনো বিশেষ স্থান আৰু সময়ত বায়ুমণ্ডলৰ অৱস্থা বা পৰিস্থিতি বুলি কোৱা হয়।\n\nA) জলবায়ু\nB) আবহাওয়া\nC) পৰ্যাৱৰণ\nD) বায়ুমণ্ডল"
    }
  ],
  "choices": [
    "জলবায়ু",
    "আবহাওয়া",
    "পৰ্যাৱৰণ",
    "বায়ুমণ্ডল"
  ],
  "target": "B",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "language": "Assamese",
    "topic": null
  }
}

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 bhasha_bench_multi_krishi \
    --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=['bhasha_bench_multi_krishi'],
    dataset_args={
        'bhasha_bench_multi_krishi': {
            # subset_list: ['Assamese', 'Bengali', 'Bodo']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)