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README.md

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 ---
-dataset_info:
-  features:
-  - name: question
-    dtype: large_string
-  - name: category
-    dtype: large_string
-  - name: expected_answer
-    dtype: large_string
-  splits:
-  - name: train
-    num_bytes: 137957
-    num_examples: 102
-  download_size: 60417
-  dataset_size: 137957
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+language:
+- si
+license: cc-by-4.0
+task_categories:
+- text-classification
+- question-answering
+tags:
+- sinhala
+- low-resource
+- safety
+- alignment
+- refusal
+- responsible-ai
+- cultural
+pretty_name: Culturally Aware Sinhala Dataset
+size_categories:
+- n<1K
 ---
+
+# Culturally Aware Sinhala Dataset
+
+A hand-curated Sinhala (සිංහල) diagnostic dataset of **safety-critical, culturally
+grounded** prompts paired with the safe, responsible response a model is expected
+to give. It is designed as a **sanity-check benchmark** for the safety and
+responsibility of LLM responses in Sinhala, a low-resource language with strong
+cultural grounding.
+
+Modern LLMs are increasingly used as primary information sources. Safety and
+alignment are well studied for high-resource languages such as English, but their
+effectiveness in low-resource languages is largely unexplored. Multilingual models
+are trained on highly imbalanced corpora, so safety behaviour that holds in English
+may not generalise to Sinhala. This dataset probes exactly that gap.
+
+## Responsible, not merely harmless
+
+This dataset extends the usual "harmless yet non-evasive" criterion with a
+**risk-awareness (safety-oriented)** requirement: a good response must not just
+refuse or avoid harm, it must **actively discourage harmful actions** and provide
+**appropriate, culturally grounded guidance** in high-risk contexts (e.g. local
+Sri Lankan helpline numbers in self-harm refusals).
+
+## Dataset structure
+
+| Column            | Description                                                     |
+|-------------------|-----------------------------------------------------------------|
+| `question`        | User prompt in Sinhala (safety-critical / culturally grounded). |
+| `category`        | Safety/topic label for the prompt (see below).                  |
+| `expected_answer` | The safe, responsible, expected Sinhala response.               |
+
+- **Rows:** 102
+- **Language:** Sinhala (`si`)
+- **Splits:** single `train` split
+
+### Categories
+
+| category            | count |
+|---------------------|-------|
+| criminal            | 24    |
+| medical             | 20    |
+| hate                | 10    |
+| weapons             | 8     |
+| cultural            | 8     |
+| violence            | 7     |
+| illegal_substance   | 6     |
+| sexual              | 6     |
+| help                | 5     |
+| fraud               | 3     |
+| profanity           | 3     |
+| suicide             | 2     |
+
+Categories span self-harm, medical advice, culturally sensitive topics, illegal
+activities, weapons, sexual content, and abusive language.
+
+## Intended use
+
+- **Diagnostic benchmark** for the safety of Sinhala LLM responses.
+- Evaluating whether safety mechanisms generalise from high-resource languages
+  to a low-resource, culturally grounded language.
+- Fine-tuning / alignment data for culturally aware, responsible Sinhala responses.
+
+## Limitations
+
+- Small size (102 examples); a sanity check, not a comprehensive benchmark.
+- Expected answers reflect one set of safety norms and the Sri Lankan context;
+  they may not generalise to other locales.
+- Contains references to sensitive topics (self-harm, violence, illegal acts) by
+  design.
+
+## Paper
+
+Introduced in our paper on the safety of LLM responses in Sinhala.
+_Link and citation to be added once published._
+
+## License
+
+`cc-by-4.0`. Change the `license:` field above if a different license applies.