LLM Evaluation · हिन्दी
Hindi Data for LLM Evaluation
Human evaluation of large language model output in Indian languages, including cultural and factual fit. In Hindi, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Hindi
- Primary metric
- Rubric scores with confidence intervals
- Typical volume
- 500-2,000 hours
Data profile required
- Native-speaker rater panels per language
- Rubric-based scoring with calibration
- Overlapping assignments for agreement
What Hindi adds to the requirement
- Four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on English-first acoustic units
- Retroflex series ट ठ ड ढ ण routinely mis-mapped to alveolar /t/ /d/ by imported lexicons
- Dialects to cover: Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi
- Urban Hindi speech is Hinglish in practice. Expect 15-40% English tokens in spontaneous speech: numbers, brands, technology terms, and whole clause switches. Any Hindi corpus that excludes English tokens will not match production traffic.
Metrics to track
- Rubric scores with confidence intervals
- Inter-rater agreement
- Failure-mode distribution
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 Hindi specifically: Public Hindi corpora skew heavily towards read newspaper text from educated urban speakers in Delhi and NCR. Rural Bihar and eastern UP speech, elderly speakers, and low-literacy speakers reading prompts aloud are largely absent.
Recommended cohort
Largest recruitment pool in the network. A 1,000-speaker Hindi cohort with balanced gender and 18-45 age bands is typically fielded across four cities to avoid a single-city accent bias.
| Dimension | Typical split | Why it matters for Hindi |
|---|---|---|
| Gender | 50 / 50 | Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition |
| Age | 18-25: 30%, 26-40: 40%, 41-60: 30% | Older speakers retain conservative Hindi forms that younger urban speakers have lost |
| Region | Uttar Pradesh / Bihar / Madhya Pradesh and others | Dialect spread across 7 recognised varieties |
| Education | Mixed, including below-graduate | Prompt-reading fluency correlates with education and skews prosody |
| Condition | Studio / quiet room / field | Match the noise profile of your deployment |
Suggested programme shape
Start with an evaluation set of 140 speakers spread across every Hindi 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 Hindi data for llm evaluation?
Public Hindi corpora skew heavily towards read newspaper text from educated urban speakers in Delhi and NCR. Rural Bihar and eastern UP speech, elderly speakers, and low-literacy speakers reading prompts aloud are largely absent.
How many Hindi 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 Hindi data for llm evaluation
Send the target metric and the languages in scope.