Papers by Maharaj Brahma
DIWALI - Diversity and Inclusivity aWare cuLture specific Items for India: Dataset and Assessment of LLMs for Cultural Text Adaptation in Indian Context (2025.emnlp-main)
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| Challenge: | Existing evaluation metrics for cultural awareness and alignment are lacking . Existing datasets for culture specific items (CSIs) focus primarily on concepts at the regional level and may contain false positives. |
| Approach: | They propose a new CSI dataset for Indian culture that measures cultural competence . they use a CSI created by LLM as Judge and human evaluations from diverse regions . |
| Outcome: | The proposed model shows that it is capable of generating culturally relevant adaptations across multiple cultural facets. |
Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights (2026.findings-acl)
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Maharaj Brahma, N J Karthika, Rajat Verma, Nagasai Saketh Naidu, Rohit Saluja, Maunendra Sankar Desarkar, Ganesh Ramakrishnan
| Challenge: | Existing tokenizers are often skewed towards high-resource languages limiting their effectiveness for linguistically diverse and morphologically rich languages. |
| Approach: | They evaluate multilingual tokenization across 17 Indic languages spanning 11 scripts and two language families. |
| Outcome: | The proposed method improves tokenization quality and vocabulary size in 17 languages . poor tokenization can lead to increase in sequence lengths, fragment meaningful units, weaken model's ability to capture linguistic structure and semantics. |
Generating Monolingual Dataset for Low Resource Language Bodo from old books using Google Keep (2022.lrec-1)
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Sanjib Narzary, Maharaj Brahma, Mwnthai Narzary, Gwmsrang Muchahary, Pranav Kumar Singh, Apurbalal Senapati, Sukumar Nandi, Bidisha Som
| Challenge: | Bodo is a scheduled Indian language spoken largely by the Boda community in Assam and other northeastern Indian states. |
| Approach: | They propose to generate a monolingual Bodo corpus from different books using Google Keep for OCR. |
| Outcome: | The proposed method generates a monolingual Bodo corpus from different books using free, accessible, and daily-usable applications. |
SelectNoise: Unsupervised Noise Injection to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages (2023.findings-emnlp)
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| Challenge: | Currently, MT systems for low-resource languages lack parallel data and monolingual data. |
| Approach: | They propose an unsupervised approach to generate noisy HRLs training data by selective candidate extraction and noise injection. |
| Outcome: | The proposed model outperforms strong baselines on 12 ELRLs in a zero-shot setting . |