A Survey on Transformers in NLP with Focus on Efficiency

• URL: http://arxiv.org/abs/2406.16893v1
• Date: Wed, 15 May 2024 10:32:41 GMT
• Title: A Survey on Transformers in NLP with Focus on Efficiency
• Authors: Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti,
• Abstract summary: This paper presents a commentary on the evolution of NLP and its applications with emphasis on their accuracy as-well-as efficiency. The goal of this survey is to determine how current NLP techniques contribute towards a sustainable society.
• Score: 2.7651063843287718
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The advent of transformers with attention mechanisms and associated pre-trained models have revolutionized the field of Natural Language Processing (NLP). However, such models are resource-intensive due to highly complex architecture. This limits their application to resource-constrained environments. While choosing an appropriate NLP model, a major trade-off exists over choosing accuracy over efficiency and vice versa. This paper presents a commentary on the evolution of NLP and its applications with emphasis on their accuracy as-well-as efficiency. Following this, a survey of research contributions towards enhancing the efficiency of transformer-based models at various stages of model development along with hardware considerations has been conducted. The goal of this survey is to determine how current NLP techniques contribute towards a sustainable society and to establish a foundation for future research.

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