bharatgenai/IndicParam

Dataset Card for IndicParam

Dataset Summary

IndicParam is a graduate-level benchmark designed to evaluate Large Language Models (LLMs) on their understanding of low- and extremely low-resource Indic languages.
The dataset contains 13,207 multiple-choice questions (MCQs) across 11 Indic languages, plus a separate Sanskrit–English code-mixed set, all sourced from official UGC-NET language question papers and answer keys.

Supported Tasks

• multiple-choice-qa: Evaluate LLMs on graduate-level multiple-choice question answering across low-resource Indic languages.
• language-understanding-evaluation: Assess language-specific competence (morphology, syntax, semantics, discourse) using explicitly labeled questions.
• general-knowledge-evaluation: Measure factual and domain knowledge in literature, culture, history, and related disciplines.
• question-type-evaluation: Analyze performance across MCQ formats (Normal MCQ, Assertion–Reason, List Matching, etc.).

Languages

IndicParam covers the following languages and one code-mixed variant:

• Low-resource (4): Nepali, Gujarati, Marathi, Odia
• Extremely low-resource (7): Dogri, Maithili, Rajasthani, Sanskrit, Bodo, Santali, Konkani
• Code-mixed: Sanskrit–English (Sans-Eng)

Scripts:

• Devanagari: Nepali, Marathi, Maithili, Konkani, Bodo, Dogri, Rajasthani, Sanskrit
• Gujarati: Gujarati
• Odia (Orya): Odia
• Ol Chiki (Olck): Santali

All questions are presented in the native script of the target language (or in code-mixed form for Sans-Eng).

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Dataset Structure

Data Instances

Each instance is a single MCQ from a UGC-NET language paper. An example (Maithili):

{
  "unique_question_id": "782166eef1efd963b5db0e8aa42b9a6e",
  "subject": "Maithili",
  "exam_name": "Question Papers of NET Dec. 2012 Maithili Paper III hindi",
  "paper_number": "Question Papers of NET Dec. 2012 Maithili Paper III hindi",
  "question_number": 1,
  "question_text": "मिथिलाभाषा रामायण' में सीताराम-विवाहक वर्णन भेल अछि -",
  "option_a": "बालकाण्डमें",
  "option_b": "अयोध्याकाण्डमे",
  "option_c": "सुन्दरकाण्डमे",
  "option_d": "उत्तरकाण्डमे",
  "correct_answer": "a",
  "question_type": "Normal MCQ"
}

Questions span:

• Language Understanding (LU): linguistics and grammar (phonology, morphology, syntax, semantics, discourse).
• General Knowledge (GK): literature, authors, works, cultural concepts, history, and related factual content.

Data Fields

• unique_question_id (string): Unique identifier for each question.
• subject (string): Name of the language / subject (e.g., Nepali, Maithili, Sanskrit).
• exam_name (string): Full exam name (UGC-NET session and subject).
• paper_number (string): Paper identifier as given by UGC-NET.
• question_number (int): Question index within the original paper.
• question_text (string): Question text in the target language (or Sanskrit–English code-mixed).
• option_a, option_b, option_c, option_d (string): Four answer options.
• correct_answer (string): Correct option label (a, b, c, or d).
• question_type (string): Question format, one of:
  • Normal MCQ
  • Assertion and Reason
  • List Matching
  • Fill in the blanks
  • Identify incorrect statement
  • Ordering

Data Splits

IndicParam is provided as a single evaluation split:

Split	Number of Questions
test	13,207

All rows are intended for evaluation only (no dedicated training/validation splits).

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Language Distribution

The benchmark follows the distribution reported in the IndicParam paper:

Language	#Questions	Script	Code
Nepali	1,038	Devanagari	npi
Marathi	1,245	Devanagari	mar
Gujarati	1,044	Gujarati	guj
Odia	577	Orya	ory
Maithili	1,286	Devanagari	mai
Konkani	1,328	Devanagari	gom
Santali	873	Olck	sat
Bodo	1,313	Devanagari	brx
Dogri	1,027	Devanagari	doi
Rajasthani	1,190	Devanagari	–
Sanskrit	1,315	Devanagari	san
Sans-Eng	971	(code-mixed)	–
Total	13,207

Each language’s questions are drawn from its respective UGC-NET language papers.

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Dataset Creation

Source and Collection

• Source: Official UGC-NET language question papers and answer keys, downloaded from the UGC-NET/NTA website.
• Scope: Multiple exam sessions and years, covering language/literature and linguistics papers for each of the 11 languages plus the Sanskrit–English code-mixed set.
• Extraction:
  • Machine-readable PDFs are parsed directly.
  • Non-selectable PDFs are processed using OCR.
  • All text is normalized while preserving the original script and content.

Annotation

In addition to the raw MCQs, each question is annotated by question type (described in detail in the paper):

• Question type:
  • Multiple-choice, Assertion–Reason, List Matching, Fill in the blanks, Identify incorrect statement, Ordering.

These annotations support fine-grained analysis of model behavior across knowledge vs. language ability and question format.

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Considerations for Using the Data

Social Impact

IndicParam is designed to:

• Enable rigorous evaluation of LLMs on under-represented Indic languages with substantial speaker populations but very limited web presence.
• Encourage culturally grounded AI systems that perform robustly on Indic scripts and linguistic phenomena.
• Highlight the performance gaps between high-resource and low-/extremely low-resource Indic languages, informing future pretraining and data collection efforts.

Users should be aware that the content is drawn from academic examinations, and may over-represent formal, exam-style language relative to everyday usage.

Evaluation Guidelines

To align with the paper and allow consistent comparison:

1. Task: Treat each instance as a multiple-choice QA item with four options.
2. Input format: Present question_text plus the four options (A–D) to the model.
3. Required output: A single option label (A, B, C, or D), with no explanation.
4. Decoding: Use greedy decoding / temperature = 0 / do_sample = False to ensure deterministic outputs.
5. Metric: Compute accuracy based on exact match between predicted option and correct_answer (case-insensitive after mapping to A–D).
6. Analysis:
   • Report overall accuracy.
   • Break down results per language.

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Additional Information

Citation Information

If you use IndicParam in your research, please cite:

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For related Hindi-only evaluation and question-type taxonomy, please also see and cite ParamBench.

License

IndicParam is released for non-commercial research and evaluation.

Acknowledgments

IndicParam was curated and annotated by the authors and native-speaker annotators as described in the paper.
We acknowledge UGC-NET/NTA for making examination materials publicly accessible, and the broader Indic NLP community for foundational tools and resources.