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Indira Gandhi Delhi Technical University for Women

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Selected work

Representative Papers

COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages

Sep 23, 2026

This study addresses the over-reliance on English pivoting and the scarcity of high-quality parallel corpora and evaluation benchmarks in machine translation for Indian languages. Departing from English-mediated approaches, we construct a parallel corpus comprising 1.16 million sentence pairs across 20 language pairs and multiple domains, sourced directly from native Indic texts. Furthermore, we introduce an expert-validated, domain-centric evaluation benchmark. Fine-tuning experiments on models such as IndicTrans2-Distilled and NLLB-200 demonstrate consistent performance improvements under both automatic and human evaluations. This work validates the effectiveness of high-quality native supervision and provides critical resources for advancing multilingual machine translation.

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Inspect India Evals: An Open Benchmarking Framework for Evaluating Large Language Models in the Indian Linguistic and Cultural Context

Jul 28, 2026

This study addresses the limitations of current mainstream large language model (LLM) evaluation benchmarks, which are predominantly English- and Western-centric and thus inadequately assess model safety, fairness, and accuracy in India’s multilingual and multicultural context. Building upon the UK AISI’s Inspect AI platform, the authors introduce the first open-source evaluation framework tailored to India’s 22 official languages. The framework encompasses six dimensions: multilingual MMLU, localized bias testing (BharatBBQ), multi-turn jailbreak resistance, cultural knowledge assessment, and safety related to digital public infrastructure (DPI), alongside an LLM-as-judge automated scoring mechanism. Evaluations of five open-source models (8B–32B parameters) reveal that Sarvam-M 24B and Gemma 2 27B both achieve 80% on an Indian fairness index, with Sarvam-M excelling in cultural knowledge and DPI compliance. While all models uniformly reject harmful multilingual prompts (100% refusal rate), their DPI safety scores vary widely (20%–100%).

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Fraud Detection System for Banking Transactions

Apr 09, 2026

This study addresses the challenges of dynamically evolving financial fraud and severe class imbalance in digital payment systems by conducting hypothesis-driven exploratory data analysis and feature engineering on the PaySim synthetic dataset, following the CRISP-DM methodology. To mitigate class imbalance, SMOTE oversampling is employed, and hyperparameter optimization is performed via GridSearchCV across multiple classifiers, including logistic regression, decision trees, random forests, and XGBoost. The resulting fraud detection framework achieves significantly enhanced detection performance while maintaining high scalability and robustness, thereby offering FinTech systems an efficient and reliable solution for real-time fraud prevention.

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Recent publications

Latest Papers

COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages

Sep 23, 2026

This study addresses the over-reliance on English pivoting and the scarcity of high-quality parallel corpora and evaluation benchmarks in machine translation for Indian languages. Departing from English-mediated approaches, we construct a parallel corpus comprising 1.16 million sentence pairs across 20 language pairs and multiple domains, sourced directly from native Indic texts. Furthermore, we introduce an expert-validated, domain-centric evaluation benchmark. Fine-tuning experiments on models such as IndicTrans2-Distilled and NLLB-200 demonstrate consistent performance improvements under both automatic and human evaluations. This work validates the effectiveness of high-quality native supervision and provides critical resources for advancing multilingual machine translation.

0 citationsRead paper

Inspect India Evals: An Open Benchmarking Framework for Evaluating Large Language Models in the Indian Linguistic and Cultural Context

Jul 28, 2026

This study addresses the limitations of current mainstream large language model (LLM) evaluation benchmarks, which are predominantly English- and Western-centric and thus inadequately assess model safety, fairness, and accuracy in India’s multilingual and multicultural context. Building upon the UK AISI’s Inspect AI platform, the authors introduce the first open-source evaluation framework tailored to India’s 22 official languages. The framework encompasses six dimensions: multilingual MMLU, localized bias testing (BharatBBQ), multi-turn jailbreak resistance, cultural knowledge assessment, and safety related to digital public infrastructure (DPI), alongside an LLM-as-judge automated scoring mechanism. Evaluations of five open-source models (8B–32B parameters) reveal that Sarvam-M 24B and Gemma 2 27B both achieve 80% on an Indian fairness index, with Sarvam-M excelling in cultural knowledge and DPI compliance. While all models uniformly reject harmful multilingual prompts (100% refusal rate), their DPI safety scores vary widely (20%–100%).

0 citationsRead paper

Fraud Detection System for Banking Transactions

Apr 09, 2026

This study addresses the challenges of dynamically evolving financial fraud and severe class imbalance in digital payment systems by conducting hypothesis-driven exploratory data analysis and feature engineering on the PaySim synthetic dataset, following the CRISP-DM methodology. To mitigate class imbalance, SMOTE oversampling is employed, and hyperparameter optimization is performed via GridSearchCV across multiple classifiers, including logistic regression, decision trees, random forests, and XGBoost. The resulting fraud detection framework achieves significantly enhanced detection performance while maintaining high scalability and robustness, thereby offering FinTech systems an efficient and reliable solution for real-time fraud prevention.

0 citationsRead paper