Validating 400+ Indian Language Translations for Multilingual LLM Evaluation
Supported a fast-paced validation campaign evaluating more than 400 translations from English to Indian languages across technical code conversations and general articles. The review focused on translation accuracy, fluency, tone, factual consistency, and surface-level issues.
400+
translation QA tasks validated, covering technical and non-technical text.
10+
Indian languages included, such as Hindi, Tamil, Kannada, Malayalam, Marathi, Punjabi, and Bengali.
15+
trainers contributed, enabling language diversity and rapid delivery.
The Challenge
Multilingual models often struggle to maintain translation fidelity across tone, syntax, and meaning, especially in domain-specific or assistant-style dialogues. The client needed:
- Reliable validation across non-technical articles and code-writing conversations
- Language-specific nuance checks for fluency, grammar, tone, and realism
- A fast-turnaround validation process without sacrificing quality
- Ability to flag malformed or low-quality inputs quickly for triage or exclusion
- Consistent methods to distinguish fluency and tone issues from factual or structural translation errors
The Approach
Turing assigned more than 15 linguistically aligned trainers to perform structured reviews based on campaign guidelines. Each translation was reviewed for:
- Accuracy: meaning preservation and correct entity transfer
- Fluency: smoothness, grammar, and native speaker readability
- Realism: natural-sounding responses in assistant contexts
- Tone: politeness, appropriateness, and instruction alignment
The team implemented a dual-tier review strategy:
- Fast review on approximately 40% of tasks to catch surface-level flaws such as typos, formatting, and instruction-following errors
- Deep review on approximately 10% of tasks with line-by-line assessment for fidelity, tone, realism, and clarity
Escalations were logged for malformed tasks and resolved in coordination with researchers. Subjective edge cases were resolved in favor of trainer judgment to preserve linguistic diversity.
Key Results
- Validated more than 400 multilingual translations within a week across technical and general content
- Escalated malformed samples and inconsistencies for filtering and dataset cleanup
- Balanced depth and speed using a hybrid QC strategy tailored for time-sensitive delivery
- Provided language-diverse trainer input to enhance output variability and realism
The Outcome
Turing’s hybrid QC workflow enabled the client to:
- Quickly validate large-scale multilingual output
- Collect actionable feedback on fluency, formatting, and realism
- Refine prompt and task structure for future translation tasks
The dataset supports benchmarking translation fidelity and tone realism in multilingual LLMs for Indian languages.
Evaluating multilingual outputs in Indian languages?
Request a sample with QA-reviewed translations, reviewer notes across tone, fluency, and realism, and language-specific feedback.
Request SampleFAQ
What languages are included?
Hindi, Tamil, Kannada, Telugu, Malayalam, Marathi, Punjabi, Gujarati, Bengali, and more.
What content types were reviewed?
Both user-assistant code conversations and general writing such as articles and FAQs.
How were tasks reviewed?
A mix of deep and fast review was applied to ensure comprehensive coverage. Deep reviews checked fidelity, tone, and logical structure, while fast reviews focused on surface-level issues such as clarity and formatting.
How were inconsistencies in subjective fields handled?
Trainers made judgment calls in ambiguous cases. Diversity was preferred over uniformity.
What’s the NDA process?
A standard mutual NDA. Turing provides the countersigned agreement within one business day.
How fast can I get a sample?
Within three business days after NDA execution.
Want to train models for code and content translation?
Use validated samples rated for fluency, tone, and instruction following.