README.md

metadata

language:
  - en
  - te
license: apache-2.0
task_categories:
  - question-answering
  - text-retrieval
  - text-generation
pretty_name: Telugu QA Paraphrases
size_categories:
  - 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}
}