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LLM Evaluation · मराठी

Marathi Data for LLM Evaluation

Human evaluation of large language model output in Indian languages, including cultural and factual fit. In Marathi, the binding constraint is usually dialect coverage and code-mixing, not raw hours.

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Evaluator scoring AI voice output against a rubric — Marathi Data for LLM Evaluation
Language
Marathi
Primary metric
Rubric scores with confidence intervals
Typical volume
500-2,000 hours
01

Data profile required

  • Native-speaker rater panels per language
  • Rubric-based scoring with calibration
  • Overlapping assignments for agreement
LLM Evaluation · MarathiData profile that moves itWhat it is scored onNative-speaker rater panels per languageRubric-based scoring with calibrationOverlapping assignments for agreementRubric scores with confidence intervalsInter-rater agreementFailure-mode distributionThe corpus is specified backwards from the right-hand column.
02

What Marathi adds to the requirement

  • Retains the retroflex lateral ळ, which has no Hindi or English equivalent and is frequently substituted with ल by non-native transcribers
  • Affricates च and ज have both alveolar and palatal realisations depending on the word, a distinction lost in Devanagari orthography
  • Dialects to cover: Standard (Puneri), Varhadi (Vidarbha), Marathwadi, Konkani-influenced coastal Marathi
  • Mumbai and Pune speech mixes Marathi, Hindi, and English in the same sentence. Marathi-only recordings collected in Pune under-represent the Mumbai reality of tri-lingual switching.
Annotators writing prompts and responses for LLM training data — supporting marathi data for llm evaluation
Annotators writing prompts and responses for LLM training data
03

Metrics to track

  • Rubric scores with confidence intervals
  • Inter-rater agreement
  • Failure-mode distribution
04

Failure modes

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

For Marathi specifically: Available Marathi speech data is dominated by standard Puneri read speech. Vidarbha, Marathwada, and coastal Konkan varieties are severely under-collected, which is exactly where deployed voice products lose accuracy.

05

Recommended cohort

A representative Marathi cohort should be split roughly 40% western Maharashtra, 25% Vidarbha, 20% Marathwada, 15% Konkan rather than concentrated in Pune.

DimensionTypical splitWhy it matters for Marathi
Gender50 / 50Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition
Age18-25: 30%, 26-40: 40%, 41-60: 30%Older speakers retain conservative Marathi forms that younger urban speakers have lost
RegionMaharashtra / Goa / parts of Karnataka and othersDialect spread across 6 recognised varieties
EducationMixed, including below-graduatePrompt-reading fluency correlates with education and skews prosody
ConditionStudio / quiet room / fieldMatch the noise profile of your deployment
06

Suggested programme shape

Start with an evaluation set of 120 speakers spread across every Marathi dialect in scope, collected before training data. Then field 500-2,000 hours of training data from disjoint speakers.

This ordering is what makes the improvement measurable rather than assumed.

Frequently asked

Is there usable public Marathi data for llm evaluation?

Available Marathi speech data is dominated by standard Puneri read speech. Vidarbha, Marathwada, and coastal Konkan varieties are severely under-collected, which is exactly where deployed voice products lose accuracy.

How many Marathi speakers do we need?

1,000-3,000 speakers for a training corpus, plus a disjoint evaluation cohort covering each dialect. Speaker count matters more than hours for generalisation.

Can you run this across multiple languages at once?

Yes. Multi-language programmes run to one master specification so per-language results stay comparable.

Scope Marathi data for llm evaluation

Send the target metric and the languages in scope.

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