5 citations · 8 across the 12 of their papers we have counts for
4 papers · 2 filters
Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models
Sina Bagheri Nezhad, Ameeta Agrawal, Rhitabrat Pokharel
Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability…
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
Sina Bagheri Nezhad, Sayan Bandyapadhyay, Ameeta Agrawal
Fairness in multi-document summarization of user-generated content remains a critical challenge in natural language processing (NLP). Existing summarization methods often fail to e…
Evaluating Multilingual Long-Context Models for Retrieval and Reasoning
Ameeta Agrawal, Andy Dang, Sina Bagheri Nezhad +2
Recent large language models (LLMs) demonstrate impressive capabilities in handling long contexts, some exhibiting near-perfect recall on synthetic retrieval tasks. However, these…
What Drives Performance in Multilingual Language Models?
Sina Bagheri Nezhad, Ameeta Agrawal
This study investigates the factors influencing the performance of multilingual large language models (MLLMs) across diverse languages. We study 6 MLLMs, including masked language…