Improve dataset card: Add metadata, paper/code links, and sample usage

README.md

@@ -1,15 +1,36 @@
 ---
 configs:
-  - config_name: IndicParam
-    data_files:
-      - path: data*
-        split: test
+- config_name: IndicParam
+  data_files:
+  - path: data*
+    split: test
 tags:
 - benchmark
+- low-resource
+- indic-languages
+task_categories:
+- question-answering
+- text-classification
+license: cc-by-nc-4.0
+language:
+- npi
+- guj
+- mar
+- ory
+- doi
+- mai
+- raj
+- san
+- brx
+- sat
+- gom
+- en
 ---
 
 ## Dataset Card for IndicParam
 
+[Paper](https://huggingface.co/papers/2512.00333) | [Code](https://github.com/ayushbits/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**.  
@@ -127,34 +148,63 @@ Each language’s questions are drawn from its respective UGC-NET language paper
 
 ### 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.
+-   **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.
+-   **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**.
 
 ---
 
+## Sample Usage
+
+The GitHub repository provides several Python scripts to evaluate models on the IndicParam dataset. You can adapt these scripts for your specific use case.
+
+Typical usage pattern, as described in the GitHub README:
+
+-   **Prepare environment**: Install Python dependencies (see `requirements.txt` if present in the GitHub repository) and configure any required API keys or model caches.
+-   **Run evaluation**: Invoke one of the scripts with your chosen model configuration and an output directory; the scripts will:
+    -   Load `data.csv`
+    -   Construct language-aware MCQ prompts
+    -   Record model predictions and compute accuracy
+
+Example scripts available in the [GitHub repository](https://github.com/ayushbits/IndicParam):
+-   `evaluate_open_models.py`: Example script to evaluate open-weight Hugging Face models on IndicParam.
+-   `evaluate_gpt_oss.py`: Example script to run the GPT-OSS-120B model on the same data.
+-   `evaluate_openrouter.py`: Example script to benchmark closed models via the OpenRouter API.
+
+Script-level arguments and options are documented via the `-h`/`--help` flags within each script.
+
+```bash
+# Example of running evaluation with an open-weight model:
+python evaluate_open_models.py --model_name_or_path google/gemma-2b --output_dir results/gemma-2b
+
+# Example of running evaluation with GPT-OSS:
+python evaluate_gpt_oss.py --model_name_or_path openai/gpt-oss-120b --output_dir results/gpt-oss-120b
+```
+
+---
+
 ## 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.
+-   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.
 
@@ -162,14 +212,14 @@ Users should be aware that the content is drawn from **academic examinations**,
 
 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**. 
+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**. 
 
 ---
 
@@ -180,6 +230,14 @@ To align with the paper and allow consistent comparison:
 If you use IndicParam in your research, please cite:
 
 ```bibtex
+@misc{maheshwari2025indicparambenchmarkevaluatellms,
+      title={IndicParam: Benchmark to evaluate LLMs on low-resource Indic Languages}, 
+      author={Ayush Maheshwari and Kaushal Sharma and Vivek Patel and Aditya Maheshwari},
+      year={2025},
+      eprint={2512.00333},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2512.00333}, 
 }
 ```
 
@@ -187,7 +245,7 @@ For related Hindi-only evaluation and question-type taxonomy, please also see an
 
 ### License
 
-IndicParam is released for **non-commercial research and evaluation**.  
+IndicParam is released for **non-commercial research and evaluation** under the [CC-BY-NC-4.0 License](https://creativecommons.org/licenses/by-nc/4.0/).
 
 ### Acknowledgments