most citedProcessing Natural Language on Embedded Devices: How Well Do Transformer Models Perform?

11 citations · 13 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Benchmarking LLMs on the Semantic Overlap Summarization Task

John Salvador, Naman Bansal, Mousumi Akter +3

Semantic Overlap Summarization (SOS) is a constrained multi-document summarization task, where the constraint is to capture the common/overlapping information between two alternati…

cs.CL2024★ 2 cited

LLMs as Meta-Reviewers' Assistants: A Case Study

Eftekhar Hossain, Sanjeev Kumar Sinha, Naman Bansal +11

One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves assimilating diverse opinions from multiple expert peer…

cs.CL2024

LLMs as On-demand Customizable Service

Souvika Sarkar, Mohammad Fakhruddin Babar, Monowar Hasan +1

Large Language Models (LLMs) have demonstrated remarkable language understanding and generation capabilities. However, training, deploying, and accessing these models pose notable…

cs.CL2023

Zero-Shot Multi-Label Topic Inference with Sentence Encoders

Souvika Sarkar, Dongji Feng, Shubhra Kanti Karmaker Santu

Sentence encoders have indeed been shown to achieve superior performances for many downstream text-mining tasks and, thus, claimed to be fairly general. Inspired by this, we perfor…

cs.CL2023

On Evaluation of Bangla Word Analogies

Mousumi Akter, Souvika Sarkar, Shubhra Kanti Karmaker Santu

This paper presents a high-quality dataset for evaluating the quality of Bangla word embeddings, which is a fundamental task in the field of Natural Language Processing (NLP). Desp…

cs.CL2023★ 11 cited

Processing Natural Language on Embedded Devices: How Well Do Transformer Models Perform?

Souvika Sarkar, Mohammad Fakhruddin Babar, Md Mahadi Hassan +2

This paper presents a performance study of transformer language models under different hardware configurations and accuracy requirements and derives empirical observations about th…