Added the IndicParam Dataset

README.md

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+## 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).
+
+---
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is a single MCQ from a UGC-NET language paper. An example (Maithili):
+
+```json
+{
+  "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).
+
+---
+
+## 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.
+
+---
+
+## 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**.
+
+---
+
+## 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**. 
+
+---
+
+## Additional Information
+
+### Citation Information
+
+If you use IndicParam in your research, please cite:
+
+```bibtex
+}
+```
+
+For related Hindi-only evaluation and question-type taxonomy, please also see and cite [ParamBench](https://huggingface.co/datasets/bharatgenai/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.

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