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###
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###
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We acknowledge UGC-NET/NTA for making examination materials publicly accessible, and the broader Indic NLP community for foundational tools and resources.
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---
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configs:
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- config_name: IndicParam
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data_files:
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- path: data*
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split: test
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tags:
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- benchmark
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---
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## Dataset Card for IndicParam
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### Dataset Summary
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IndicParam is a graduate-level benchmark designed to evaluate Large Language Models (LLMs) on their understanding of **low- and extremely low-resource Indic languages**.
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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.
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### Supported Tasks
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- **`multiple-choice-qa`**: Evaluate LLMs on graduate-level multiple-choice question answering across low-resource Indic languages.
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- **`language-understanding-evaluation`**: Assess language-specific competence (morphology, syntax, semantics, discourse) using explicitly labeled questions.
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- **`general-knowledge-evaluation`**: Measure factual and domain knowledge in literature, culture, history, and related disciplines.
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- **`question-type-evaluation`**: Analyze performance across MCQ formats (Normal MCQ, Assertion–Reason, List Matching, etc.).
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### Languages
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IndicParam covers the following languages and one code-mixed variant:
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- **Low-resource (4)**: Nepali, Gujarati, Marathi, Odia
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- **Extremely low-resource (7)**: Dogri, Maithili, Rajasthani, Sanskrit, Bodo, Santali, Konkani
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- **Code-mixed**: Sanskrit–English (Sans-Eng)
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Scripts:
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- **Devanagari**: Nepali, Marathi, Maithili, Konkani, Bodo, Dogri, Rajasthani, Sanskrit
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- **Gujarati**: Gujarati
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- **Odia (Orya)**: Odia
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- **Ol Chiki (Olck)**: Santali
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All questions are presented in the **native script** of the target language (or in code-mixed form for Sans-Eng).
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---
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## Dataset Structure
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### Data Instances
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Each instance is a single MCQ from a UGC-NET language paper. An example (Maithili):
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```json
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{
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"unique_question_id": "782166eef1efd963b5db0e8aa42b9a6e",
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"subject": "Maithili",
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"exam_name": "Question Papers of NET Dec. 2012 Maithili Paper III hindi",
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"paper_number": "Question Papers of NET Dec. 2012 Maithili Paper III hindi",
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"question_number": 1,
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"question_text": "मिथिलाभाषा रामायण' में सीताराम-विवाहक वर्णन भेल अछि -",
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"option_a": "बालकाण्डमें",
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"option_b": "अयोध्याकाण्डमे",
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"option_c": "सुन्दरकाण्डमे",
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"option_d": "उत्तरकाण्डमे",
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"correct_answer": "a",
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"question_type": "Normal MCQ"
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}
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```
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Questions span:
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- **Language Understanding (LU)**: linguistics and grammar (phonology, morphology, syntax, semantics, discourse).
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- **General Knowledge (GK)**: literature, authors, works, cultural concepts, history, and related factual content.
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### Data Fields
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- **`unique_question_id`** *(string)*: Unique identifier for each question.
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- **`subject`** *(string)*: Name of the language / subject (e.g., `Nepali`, `Maithili`, `Sanskrit`).
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- **`exam_name`** *(string)*: Full exam name (UGC-NET session and subject).
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- **`paper_number`** *(string)*: Paper identifier as given by UGC-NET.
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- **`question_number`** *(int)*: Question index within the original paper.
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- **`question_text`** *(string)*: Question text in the target language (or Sanskrit–English code-mixed).
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- **`option_a`**, **`option_b`**, **`option_c`**, **`option_d`** *(string)*: Four answer options.
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- **`correct_answer`** *(string)*: Correct option label (`a`, `b`, `c`, or `d`).
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- **`question_type`** *(string)*: Question format, one of:
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- `Normal MCQ`
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- `Assertion and Reason`
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- `List Matching`
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- `Fill in the blanks`
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- `Identify incorrect statement`
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- `Ordering`
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### Data Splits
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IndicParam is provided as a **single evaluation split**:
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| Split | Number of Questions |
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| ----- | ------------------- |
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| test | 13,207 |
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All rows are intended for **evaluation only** (no dedicated training/validation splits).
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---
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## Language Distribution
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The benchmark follows the distribution reported in the IndicParam paper:
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| Language | #Questions | Script | Code |
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| ------------- | ---------- | -------- | ---- |
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| Nepali | 1,038 | Devanagari | npi |
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| Marathi | 1,245 | Devanagari | mar |
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| Gujarati | 1,044 | Gujarati | guj |
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| Odia | 577 | Orya | ory |
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| Maithili | 1,286 | Devanagari | mai |
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| Konkani | 1,328 | Devanagari | gom |
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| Santali | 873 | Olck | sat |
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| Bodo | 1,313 | Devanagari | brx |
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| Dogri | 1,027 | Devanagari | doi |
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| Rajasthani | 1,190 | Devanagari | – |
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| Sanskrit | 1,315 | Devanagari | san |
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| Sans-Eng | 971 | (code-mixed) | – |
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| **Total** | **13,207** | | |
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Each language’s questions are drawn from its respective UGC-NET language papers.
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---
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## Dataset Creation
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### Source and Collection
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- **Source**: Official UGC-NET language question papers and answer keys, downloaded from the UGC-NET/NTA website.
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- **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.
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- **Extraction**:
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- Machine-readable PDFs are parsed directly.
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- Non-selectable PDFs are processed using OCR.
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- All text is normalized while preserving the original script and content.
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### Annotation
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In addition to the raw MCQs, each question is annotated by question type (described in detail in the paper):
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- **Question type**:
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- Multiple-choice, Assertion–Reason, List Matching, Fill in the blanks, Identify incorrect statement, Ordering.
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These annotations support fine-grained analysis of model behavior across **knowledge vs. language ability** and **question format**.
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---
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## Considerations for Using the Data
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### Social Impact
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IndicParam is designed to:
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- Enable rigorous evaluation of LLMs on **under-represented Indic languages** with substantial speaker populations but very limited web presence.
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- Encourage **culturally grounded** AI systems that perform robustly on Indic scripts and linguistic phenomena.
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- Highlight the performance gaps between high-resource and low-/extremely low-resource Indic languages, informing future pretraining and data collection efforts.
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Users should be aware that the content is drawn from **academic examinations**, and may over-represent formal, exam-style language relative to everyday usage.
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### Evaluation Guidelines
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To align with the paper and allow consistent comparison:
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1. **Task**: Treat each instance as a multiple-choice QA item with four options.
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2. **Input format**: Present `question_text` plus the four options (`A–D`) to the model.
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3. **Required output**: A single option label (`A`, `B`, `C`, or `D`), with no explanation.
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4. **Decoding**: Use **greedy decoding / temperature = 0 / `do_sample = False`** to ensure deterministic outputs.
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5. **Metric**: Compute **accuracy** based on exact match between predicted option and `correct_answer` (case-insensitive after mapping to A–D).
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6. **Analysis**:
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- Report **overall accuracy**.
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- Break down results **per language**.
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---
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## Additional Information
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### Citation Information
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If you use IndicParam in your research, please cite:
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```bibtex
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}
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```
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For related Hindi-only evaluation and question-type taxonomy, please also see and cite [ParamBench](https://huggingface.co/datasets/bharatgenai/ParamBench).
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### License
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IndicParam is released for **non-commercial research and evaluation**.
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### Acknowledgments
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IndicParam was curated and annotated by the authors and native-speaker annotators as described in the paper.
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We acknowledge UGC-NET/NTA for making examination materials publicly accessible, and the broader Indic NLP community for foundational tools and resources.
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