Zobrazeno 1 - 10
of 3 838
pro vyhledávání: '"A. Raviraj"'
Autor:
Joshi, Raviraj, Singla, Kanishk, Kamath, Anusha, Kalani, Raunak, Paul, Rakesh, Vaidya, Utkarsh, Chauhan, Sanjay Singh, Wartikar, Niranjan, Long, Eileen
Multilingual LLMs support a variety of languages; however, their performance is suboptimal for low-resource languages. In this work, we emphasize the importance of continued pre-training of multilingual LLMs and the use of translation-based synthetic
Externí odkaz:
http://arxiv.org/abs/2410.14815
Autor:
Deshmukh, Pranita, Kulkarni, Nikita, Kulkarni, Sanhita, Manghani, Kareena, Kale, Geetanjali, Joshi, Raviraj
The demand for sophisticated natural language processing (NLP) methods, particularly Named Entity Recognition (NER), has increased due to the exponential growth of Marathi-language digital content. In particular, NER is essential for recognizing dist
Externí odkaz:
http://arxiv.org/abs/2410.09192
We present the MahaSUM dataset, a large-scale collection of diverse news articles in Marathi, designed to facilitate the training and evaluation of models for abstractive summarization tasks in Indic languages. The dataset, containing 25k samples, wa
Externí odkaz:
http://arxiv.org/abs/2410.09184
Autor:
Patil, Rajlaxmi, Kulkarni, Aditya Ashutosh, Ghatage, Ruturaj, Endait, Sharvi, Kale, Geetanjali, Joshi, Raviraj
In the domain of education, the integration of,technology has led to a transformative era, reshaping traditional,learning paradigms. Central to this evolution is the automation,of grading processes, particularly within the STEM domain encompassing Sc
Externí odkaz:
http://arxiv.org/abs/2409.15749
This study examines the effectiveness of layer pruning in creating efficient Sentence BERT (SBERT) models. Our goal is to create smaller sentence embedding models that reduce complexity while maintaining strong embedding similarity. We assess BERT mo
Externí odkaz:
http://arxiv.org/abs/2409.14168
Autor:
Mirashi, Aishwarya, Lingayat, Purva, Sonavane, Srushti, Padhiyar, Tejas, Joshi, Raviraj, Kale, Geetanjali
The rise of large transformer models has revolutionized Natural Language Processing, leading to significant advances in tasks like text classification. However, this progress demands substantial computational resources, escalating training duration,
Externí odkaz:
http://arxiv.org/abs/2409.14162
Large Language Models (LLMs) have made significant progress in incorporating Indic languages within multilingual models. However, it is crucial to quantitatively assess whether these languages perform comparably to globally dominant ones, such as Eng
Externí odkaz:
http://arxiv.org/abs/2409.08706
This paper introduces Chain of Translation Prompting (CoTR), a novel strategy designed to enhance the performance of language models in low-resource languages. CoTR restructures prompts to first translate the input context from a low-resource languag
Externí odkaz:
http://arxiv.org/abs/2409.04512
Machine translation in low-resource language pairs faces significant challenges due to the scarcity of parallel corpora and linguistic resources. This study focuses on the case of English-Marathi language pairs, where existing datasets are notably no
Externí odkaz:
http://arxiv.org/abs/2409.02712
Autor:
Sreenivas, Sharath Turuvekere, Muralidharan, Saurav, Joshi, Raviraj, Chochowski, Marcin, Patwary, Mostofa, Shoeybi, Mohammad, Catanzaro, Bryan, Kautz, Jan, Molchanov, Pavlo
We present a comprehensive report on compressing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, using pruning and distillation. We explore two distinct pruning strategies: (1) depth pruning and (2) joint hidden/at
Externí odkaz:
http://arxiv.org/abs/2408.11796