22 citations · 33 across the 6 of their papers we have counts for
10 papers · 1 filter
There is No Theoretical Curse of Multilinguality For Embedding Space Structure
Niyati Bafna, Neha Verma, Vilém Zouhar +2
A central goal of multilingual NLP is to achieve high monolingual performance per language and cross-lingual alignment for large-scale language coverage with a multilingual model.…
ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging
Neha Verma, Nikhil Mehta, Shao-Chuan Wang +7
Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetting of their general, languag…
Merging Feed-Forward Sublayers for Compressed Transformers
Neha Verma, Kenton Murray, Kevin Duh
With the rise and ubiquity of larger deep learning models, the need for high-quality compression techniques is growing in order to deploy these models widely. The sheer parameter c…
Merging Text Transformer Models from Different Initializations
Neha Verma, Maha Elbayad
Recent work on permutation-based model merging has shown impressive low- or zero-barrier mode connectivity between models from completely different initializations. However, this l…
Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer
Elizabeth Salesky, Neha Verma, Philipp Koehn +1
We introduce and demonstrate how to effectively train multilingual machine translation models with pixel representations. We experiment with two different data settings with a vari…
Exploring Representational Disparities Between Multilingual and Bilingual Translation Models
Neha Verma, Kenton Murray, Kevin Duh
Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sh…