nlpctx/telugu-qa-codemixed

language:

• —en
• —te license: apache-2.0 task_categories:
• —question-answering
• —text-retrieval
• —text-generation prettyname: Telugu QA Paraphrases sizecategories:
• —1K<n<10K ---

Telugu QA Paraphrases

A synthetic multilingual query-rewriting dataset for evaluating retrieval robustness under Telugu-English code mixing.

Dataset Description

This dataset extends an existing Telugu QA dataset by generating multiple query variants with increasing levels of Telugu-English code mixing.

Each example contains:

• —question : Original English question
• —answer : Ground-truth answer
• —level_0 : English paraphrase
• —level_1 : Light Telugu-English code mixing
• —level_2 : Moderate Telugu-English code mixing
• —level_3 : Heavy Telugu-English code mixing
• —level_4 : Romanized Telugu
• —level_5 : Telugu script

Motivation

Multilingual embedding models are often evaluated on pure English or fully translated queries.

Real-world users frequently write:

• —Telugu-English code mixed text
• —Romanized Telugu
• —Mixed-script queries

This dataset enables evaluation of retrieval robustness across progressively increasing code-mixing levels.

Example

{
  "question": "How do I apply for a passport in India?",
  "answer": "...",
  "level_0": "How can I apply for an Indian passport?",
  "level_1": "భారతదేశంలో పాస్పోర్ట్ ఎలా అప్లై చెయ్యాలి?",
  "level_2": "India-lo passport apply process enti?",
  "level_3": "భారత్ పాస్పోర్ట్ అప్లికేషన్ ఎలా చేయాలి?",
  "level_4": "India passport apply ela?",
  "level_5": "భారతదేశ పాస్పోర్ట్ కోసం ఎలా అప్లై చేయాలి?"
}

Intended Uses

• —Multilingual retrieval
• —Dense retrieval evaluation
• —RAG benchmarking
• —Code-mixed query understanding
• —Embedding model robustness analysis

Source Dataset

This dataset was generated from:

https://huggingface.co/datasets/gurumurthy3/Legal-FAQ

Please cite and credit the original dataset creators.

Generation

Query variants were generated using Sarvam-M and subsequently filtered for semantic preservation.

Limitations

The generated paraphrases may contain:

• —Imperfect transliteration
• —Script mixing
• —Translation artifacts
• —Semantic drift in a small number of examples

Manual validation is recommended for benchmark creation.

Citation

@dataset{telugu_qa_paraphrases,
  title={Telugu QA Paraphrases},
  author={Suhas Koheda},
  year={2026}
}