aidataservices.inAI data collection · India

LLM human data · Machine Translation

Human Data for LLM Projects for Machine Translation

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 machine translation, the specification is driven by one thing: human adequacy and fluency scores.

Request a dataset quoteReply within one working day
Abstract visualisation of translation between two Indian languages — Human Data for LLM Projects for Machine Translation
Service
LLM human data
Use case
Machine Translation
Primary metric
Human adequacy and fluency scores
01

Required data profile

  • Sentence-aligned parallel corpora
  • Register-matched to your product
  • Enforced terminology glossary
LLM human data — Machine Translation · 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
Annotator labelling audio segments and speaker turns — supporting human data for llm projects for machine translation
Annotator labelling audio segments and speaker turns
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

  • Human adequacy and fluency scores
  • Terminology compliance rate
  • Back-translation divergence
05

Failure modes to design out

  • Pivoting everything through English
  • Post-edited machine output passed off as human translation

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 machine translation?

It covers sentence-aligned parallel corpora. Most machine translation 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 machine translation

Send the metric you need to move.

Request a dataset quote