Update README.md

README.md

@@ -1,52 +1,90 @@
 ---
 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
+- san
+- brx
+- sat
+- gom
 ---
 
+
 ## Dataset Card for IndicParam
 
+
+[Paper](https://arxiv.org/abs/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**.  
 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",
@@ -64,13 +102,17 @@ Each instance is a single MCQ from a UGC-NET language paper. An example (Maithil
 }
 ```
 
+
 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).  
@@ -87,22 +129,30 @@ Questions span:
   - `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 |
@@ -119,77 +169,148 @@ The benchmark follows the distribution reported in the IndicParam paper:
 | 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.
+
+-   **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
+
+
+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`: script to run the GPT-OSS-120B model on the same data.
+-   `evaluate_openrouter.py`: 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.
 
+
 ### 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**. 
+
+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:
 
+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}, 
 }
 ```
 
-For related Hindi-only evaluation and question-type taxonomy, please also see and cite [ParamBench](https://huggingface.co/datasets/bharatgenai/ParamBench).
 
 ### License
+CCbyNC
+IndicParam is released for **non-commercial research and evaluation** 
 
-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.