  Named Entity Recognition(NER) is a task of recognizing entities at a token
level in a sentence. This paper focuses on solving NER tasks in a multilingual
setting for complex named entities. Our team, LLM-RM participated in the
recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual
Complex Named Entity Recognition. We approach the problem by leveraging
cross-lingual representation provided by fine-tuning XLM-Roberta base model on
datasets of all of the 12 languages provided -- Bangla, Chinese, English,
Farsi, French, German, Hindi, Italian, Portuguese, Spanish, Swedish and
Ukrainian
