3 papers
cs.CL2025
Adapting Multilingual Models to Code-Mixed Tasks via Model Merging
Prashant Kodali, Vaishnavi Shivkumar, Swarang Joshi +3
We study model merging as a practical alternative to conventional adaptation strategies for code-mixed NLP. Starting from a multilingual base model, we: (i) perform continued pre-t…
cs.CL2025
How Deep Is Representational Bias in LLMs? The Cases of Caste and Religion
Agrima Seth, Monojit Choudhary, Sunayana Sitaram +3
Representational bias in large language models (LLMs) has predominantly been measured through single-response interactions and has focused on Global North-centric identities like r…
cs.CL2025
sPhinX: Sample Efficient Multilingual Instruction Fine-Tuning Through N-shot Guided Prompting
Sanchit Ahuja, Kumar Tanmay, Hardik Hansrajbhai Chauhan +9
Despite the remarkable success of large language models (LLMs) in English, a significant performance gap remains in non-English languages. To address this, we introduce a novel app…