Datasets:

sengtha
/

khmer-qa

license: cc-by-sa-4.0
language:
  - km
task_categories:
  - question-answering
  - text-generation
tags:
  - khmer
  - question-answering
  - instruction-tuning
  - sft
  - synthetic
  - rag
  - iany
size_categories:
  - 1K<n<10K
pretty_name: Khmer Q&A (context-grounded)

Khmer Q&A — context-grounded (khmer-qa)

An open Khmer question-answering / instruction-tuning dataset for fine-tuning Khmer answering LLMs. Each example is a (context, question, answer) triple where the answer is grounded in the context — ideal for teaching a model to answer from retrieved passages (RAG-style), in Khmer.

Built for iAny, the offline, on-device Khmer AI platform, and released open source. It's used to SFT iAny's on-device Khmer LLM (and can train larger models from the same data).

What's in it

A single file, data.json — a JSON array of ~2,500 rows:

Field	Type	Description
`context`	string	a factual Khmer paragraph (the grounding source)
`question`	string	a Khmer question about the context
`answer`	string	the Khmer answer — a short span or a 1–3 sentence explanation/summary
`type`	string	task type: `extract` · `explain` · `summarize`

The mix of task types is deliberate: an earlier extractive-only set made models answer too tersely (single-word spans). This set adds fuller explain/summarize answers so an SFT teaches the model to answer completely, not just grab a word.

Example:

{
  "context": "ភ្នំពេញ គឺជារាជធានីរបស់ប្រទេសកម្ពុជា។",
  "question": "តើរាជធានីរបស់កម្ពុជាឈ្មោះអ្វី?",
  "answer": "ភ្នំពេញ",
  "type": "extract"
}

How it was built

Source passages: clean factual paragraphs from Khmer Wikipedia (wikimedia/wikipedia, 20231101.km).
Q&A generation: synthesized by Qwen2.5-Instruct (7B / 14B) with few-shot Khmer prompts, one prompt per task type.
Grounding filter: each answer must overlap the context by a character-5-gram threshold (strict for extract, looser for explain/summarize), which blocks made-up facts while allowing paraphrase.

Full recipe: github.com/sengtha/iAny · docs/BUILD-KHMER-QA-DATASET.md.

Load it

from datasets import load_dataset
ds = load_dataset(
    "json",
    data_files="https://huggingface.co/datasets/sengtha/khmer-qa/resolve/main/data.json",
    split="train",
)
print(ds[0])

Intended use

Supervised fine-tuning (SFT) / instruction-tuning of Khmer LLMs to answer grounded in a provided context. Format a training prompt from context + question and target answer; the type field is for analysis/balancing and can be ignored by the trainer.

Limitations & responsible use

Synthetic. Answers are model-generated (grounded on Wikipedia, not human-verified) — expect some noise despite the grounding filter. Review a sample before relying on it.
Domain: general/encyclopedic (Khmer Wikipedia). Mix in your own domain passages for domain-specific Q&A.
Not a benchmark or a source of ground-truth facts — it's SFT training data.

License & attribution

Released under CC-BY-SA-4.0: the context passages derive from Khmer Wikipedia (CC-BY-SA-4.0), so the dataset inherits it and share-alike — attribute Wikipedia and share derivatives alike. Q&A generated with Qwen2.5 (Apache-2.0). Built and released by iAny (E-KHMER Technology).