Exploring Two-Phase Continual Instruction Fine-tuning for Multilingual Adaptation in Large Language Models
A two-phase instruction-finetuning approach for extending multilingual ability while retaining previously learned capabilities.
Publications
Research
My work studies how language models can gain new languages and capabilities efficiently, retain existing knowledge, and be evaluated across diverse settings.
10 publications across ACL, EMNLP, NAACL, EACL, and focused workshops.
My recent work concentrates on multilingual adaptation, continual learning, parameter-efficient finetuning, model behavior, and broad evaluation. Earlier work covers multilingual inference, semantic parsing, and computational biology.
* Equal contribution
2026
2025
Improving Cross Lingual Transfer by Pretraining with Active Forgetting
Pretraining with active forgetting to improve transfer when decoder language models are adapted to new languages.
Improving Consistency in LLM Inference using Probabilistic Tokenization
A probabilistic tokenization strategy for improving the consistency of language-model outputs across repeated inference.
2024
MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models
A systematic evaluation of parameter-efficient finetuning choices across multilingual downstream tasks.
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
A broad evaluation benchmark spanning languages, modalities, model families, and task types.