17 citations · 31 across the 8 of their papers we have counts for
10 papers · 1 filter
Learning Faster with Better Tokens: Parameter-Efficient Vocabulary Adaptation for Specialized Text Summarization
Gunjan Balde, Soumyadeep Roy, Mainack Mondal +1
Large language models pretrained on general-domain corpora often exhibit tokenization inefficiencies when applied to specialized domains. Although continual pretraining for domain…
"Where does it hurt?" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues
Tom Röhr, Soumyadeep Roy, Fares Al Mohamad +4
In a doctor-patient dialogue, the primary objective of physicians is to diagnose patients and propose a treatment plan. Medical doctors guide these conversations through targeted q…
Evaluation of LLMs in Medical Text Summarization: The Role of Vocabulary Adaptation in High OOV Settings
Gunjan Balde, Soumyadeep Roy, Mainack Mondal +1
Large Language Models (LLMs) recently achieved great success in medical text summarization by simply using in-context learning. However, these recent efforts do not perform fine-gr…
Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Models
Gunjan Balde, Soumyadeep Roy, Mainack Mondal +1
In this work, we show a fundamental limitation in vocabulary adaptation approaches that use Byte-Pair Encoding (BPE) tokenization scheme for fine-tuning pretrained language models…
Unlocking Efficiency: Adaptive Masking for Gene Transformer Models
Soumyadeep Roy, Shamik Sural, Niloy Ganguly
Gene transformer models such as Nucleotide Transformer, DNABert, and LOGO are trained to learn optimal gene sequence representations by using the Masked Language Modeling (MLM) tra…
MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarization
Gunjan Balde, Soumyadeep Roy, Mainack Mondal +1
This work presents a dynamic vocabulary adaptation strategy, MEDVOC, for fine-tuning pre-trained language models (PLMs) like BertSumAbs, BART, and PEGASUS for improved medical text…