LLM Companies · LLM Evaluation
LLM Evaluation Data for LLM Companies
Foundation and applied LLM teams that need Indian-language human data with provable provenance, covering languages their web crawl barely touched. Human evaluation of large language model output in Indian languages, including cultural and factual fit.
- Buyer
- LLM Companies
- Use case
- LLM Evaluation
- Metric
- Rubric scores with confidence intervals
Where the two meet
Web-scraped Indian-language text is thin, noisy and heavily transliterated 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 every item traceable to a screened, consenting contributor?
- Can contributors be screened by domain expertise, not just language?
- Is there an adjudication process for disagreement on subjective tasks?
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
- Auditable provenance records
- Contributor consent for model training and distribution
- No third-party or scraped content in deliverables
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.