aidataservices.inAI data collection · India

LLM human data · LLM Evaluation

Human Data for LLM Projects for LLM Evaluation

Human-generated text and speech for LLM training and evaluation in Indian languages: prompts, preference rankings, instruction-response pairs, red-teaming and cultural-fit review. Applied to llm evaluation, the specification is driven by one thing: rubric scores with confidence intervals.

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Evaluator scoring AI voice output against a rubric — Human Data for LLM Projects for LLM Evaluation
Service
LLM human data
Use case
LLM Evaluation
Primary metric
Rubric scores with confidence intervals
01

Required data profile

  • Native-speaker rater panels per language
  • Rubric-based scoring with calibration
  • Overlapping assignments for agreement
LLM human data — LLM Evaluation · written into the SOW before recordingTask typesPrompt writing, response ranking, instruction-response pair…LanguagesAny language in the network, including code-mixed HinglishContributorsScreened by domain, education band and language proficiencyAgreementOverlapping assignments with adjudicationProvenancePer-item contributor and time recordsYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
Task typesPrompt writing, response ranking, instruction-response pairs, adversarial testing
LanguagesAny language in the network, including code-mixed Hinglish
ContributorsScreened by domain, education band and language proficiency
AgreementOverlapping assignments with adjudication
ProvenancePer-item contributor and time records
Studio-grade voice recording session for text-to-speech training data — supporting human data for llm projects for llm evaluation
Studio-grade voice recording session for text-to-speech training data
03

Process

  • Task specification and rubric design
  • Contributor screening against the rubric
  • Calibration round with feedback
  • Production with overlap and gold items
  • Adjudication and delivery
04

Metrics this feeds

  • Rubric scores with confidence intervals
  • Inter-rater agreement
  • Failure-mode distribution
05

Failure modes to design out

  • Raters who are fluent but not native in the variety
  • Rubrics written in English and applied to non-English output without localisation

Every item is traceable to a screened contributor, which matters when a model vendor audits your data provenance.

06

Deliverables

  • Task data in your schema
  • Rubric and calibration results
  • Contributor metadata (anonymised)
  • Agreement statistics

Frequently asked

Is llm human data the right service for llm evaluation?

It covers native-speaker rater panels per language. Most llm evaluation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.

What languages are available?

All 14 languages in the network plus Indian English accent bands.

How is the evaluation set handled?

Collected first, from speakers disjoint from the training cohort, so improvement is measurable.

Scope llm human data for llm evaluation

Send the metric you need to move.

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