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

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BhashaBench-Multi (Ayurveda)

Overview

BhashaBench-Multi (Ayurveda) is a domain-specific multiple-choice benchmark evaluating LLM knowledge of Ayurvedic medicine 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 Ayurvedic medicine 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_ayur`
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	329,186
Prompt Length (Mean)	317.8 chars
Prompt Length (Min/Max)	220 / 8370 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
`Assamese`	14,963	325.76	229	4447
`Bengali`	14,963	313.28	231	1933
`Bodo`	14,963	315.55	222	1795
`Dogri`	14,963	308.39	225	1974
`Gujarati`	14,963	313.22	227	1526
`Hindi`	14,963	313.66	230	2018
`Kannada`	14,963	316.92	230	8305
`Kashmiri`	14,963	339.03	243	2102
`Konkani`	14,963	307.78	227	1819
`Maithili`	14,963	305.52	225	2142
`Malayalam`	14,963	330.73	236	1862
`Manipuri`	14,963	332.17	234	2247
`Marathi`	14,963	312.05	229	8370
`Nepali`	14,963	312.62	229	1825
`Oriya`	14,963	312.36	226	4092
`Punjabi`	14,963	309.98	220	926
`Sanskrit`	14,963	312.86	225	1232
`Santhali`	14,963	334.48	234	2283
`Sindhi`	14,963	309.44	226	852
`Tamil`	14,963	331.24	236	1031
`Telugu`	14,963	319.9	232	911
`Urdu`	14,963	314.71	227	921

Sample Example

Subset: Assamese

{
  "input": [
    {
      "id": "4ac474ca",
      "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\nইমিউনজনিত বিকাৰসমূহৰ ভিতৰত আছে .....\n\nA) অতিরিক্ত সংবেদনশীলতা\nB) স্বয়ং-প্রতিরোধ ক্ষমতা জনিত ৰোগ\nC) রোগ প্রতিরোধ ক্ষমতাৰ অভাৱ\nD) এই সকলোবোৰ।"
    }
  ],
  "choices": [
    "অতিরিক্ত সংবেদনশীলতা",
    "স্বয়ং-প্রতিরোধ ক্ষমতা জনিত ৰোগ",
    "রোগ প্রতিরোধ ক্ষমতাৰ অভাৱ",
    "এই সকলোবোৰ।"
  ],
  "target": "D",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "language": "Assamese",
    "topic": "Kayachikitsa"
  }
}

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

run_task(task_cfg=task_cfg)

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