1 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
LMK > CLS: Landmark Pooling for Dense Embeddings
Meet Doshi, Aashka Trivedi, Vishwajeet Kumar +5
Representation learning is central to many downstream tasks such as search, clustering, classification, and reranking. State-of-the-art sequence encoders typically collapse a varia…
Granite Embedding R2 Models
Parul Awasthy, Aashka Trivedi, Yulong Li +17
We introduce the Granite Embedding R2 models, a comprehensive family of high-performance English encoder-based embedding models engineered for enterprise-scale dense retrieval appl…
How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?
Pranav Gaikwad, Meet Doshi, Raj Dabre +1
Machine Translation (MT) between linguistically dissimilar languages is challenging, especially due to the scarcity of parallel corpora. Prior works suggest that pivoting through a…
Pretraining Language Models Using Translationese
Meet Doshi, Raj Dabre, Pushpak Bhattacharyya
In this paper, we explore the utility of translationese as synthetic data created using machine translation for pre-training language models (LMs) for low-resource languages (LRLs)…
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities
Settaluri Lakshmi Sravanthi, Meet Doshi, Tankala Pavan Kalyan +3
LLMs have demonstrated remarkable capability for understanding semantics, but they often struggle with understanding pragmatics. To demonstrate this fact, we release a Pragmatics U…