Contemporary Approaches in Evolving Language Models

Abstract

This article provides a comprehensive survey of contemporary language modeling approaches within the realm of natural language processing (NLP) tasks. This paper conducts an analytical exploration of diverse methodologies employed in the creation of language models. This exploration encompasses the architecture, training processes, and optimization strategies inherent in these models. The detailed discussion covers various models ranging from traditional n-gram and hidden Markov models to state-of-the-art neural network approaches such as BERT, GPT, LLAMA, and Bard. This article delves into different modifications and enhancements applied to both standard and neural network architectures for constructing language models. Special attention is given to addressing challenges specific to agglutinative languages within the context of developing language models for various NLP tasks, particularly for Arabic and Turkish. The research highlights that contemporary transformer-based methods demonstrate results comparable to those achieved by traditional methods employing Hidden Markov Models. These transformer-based approaches boast simpler configurations and exhibit faster performance during both training and analysis. An integral component of the article is the examination of popular and actively evolving libraries and tools essential for constructing language models. Notable tools such as NLTK, TensorFlow, PyTorch, and Gensim are reviewed, with a comparative analysis considering their simplicity and accessibility for implementing diverse language models. The aim is to provide readers with insights into the landscape of contemporary language modeling methodologies and the tools available for their implementation.

Enhancing Product Design through AI-Driven Sentiment Analysis of Amazon Reviews Using BERT

“…These metrics encompass facets such as generalization, robustness, reasoning abilities, language understanding, and generation capabilities of language models. By encompassing such a wide range of assessments, LLAMA offers a holistic approach to evaluating and comparing the performance and functionalities of language models within the NLP domain [18].…”

“…Diversifying its capabilities, T5 spans various iterations, including the small, base, and large models, and T5-3b and T5-11b, each trained with parameter counts ranging from 3 billion to 11 billion. This expansive pre-training contributes to T5's adaptability and ease of fine tuning for domain-specific NLP tasks, marking it as a versatile and customizable model for various text-based applications [18].…”