ML Research Groups · LLM Evaluation
LLM Evaluation Data for ML Research Groups
Academic and industrial research teams building benchmarks and studying low-resource Indian languages, where documentation and reproducibility matter as much as volume. Human evaluation of large language model output in Indian languages, including cultural and factual fit.
- Buyer
- ML Research Groups
- Use case
- LLM Evaluation
- Metric
- Rubric scores with confidence intervals
Where the two meet
Low-resource languages have no usable public data at all That is a llm evaluation problem, and it is solved by data shaped like this:
- Native-speaker rater panels per language
- Rubric-based scoring with calibration
- Overlapping assignments for agreement
Your evaluation criteria
- Is the collection protocol documented well enough to publish?
- Are speaker demographics reported in aggregate for dataset cards?
- Can the data be released openly, and under what consent terms?
Metrics
- Rubric scores with confidence intervals
- Inter-rater agreement
- Failure-mode distribution
Pitfalls
- Raters who are fluent but not native in the variety
- Rubrics written in English and applied to non-English output without localisation
Contract points
- Open-release-compatible consent
- Dataset card material provided with delivery
- Attribution and citation terms
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.