Large Language Models (LLMs) are transforming the field of natural language processing (NLP). These models are trained on vast texts to generate, understand, and manipulate human-like text. LLMs have been successful in capturing intricate linguistic nuances and semantic contexts, making them adept at various tasks such as text generation and answering questions. In this article, we discuss the architecture behind LLMs, which primarily use transformer-based designs, and highlight notable models like GPT and BERT. We also provide an evaluation methodology for comparing our work with NLP2REST, a previous study on RESTful services for NLP tasks. To promote reproducibility and future works, we make available all associated code, data, and results in an artifact repository.
Introduction
Large Language Models (LLMs) have gained significant attention in recent years due to their impressive capabilities in natural language processing (NLP). These models have been trained on vast texts to generate, understand, and manipulate human-like text with remarkable accuracy. In this article, we delve into the world of LLMs, exploring their architecture, notable models, and evaluation methodology.
Architecture
LLMs are primarily built using transformer-based designs [35]. The transformer architecture was introduced in 2017 by Vaswani et al. [35] and has since become the de facto standard for NLP tasks. The transformer design consists of an encoder and a decoder, each comprising multiple identical layers. Each layer in the encoder and decoder contains self-attention mechanisms that allow the model to weigh the importance of different words or phrases in a text [35]. This architecture enables LLMs to capture contextual dependencies between words and handle long-range dependencies, making them highly effective at understanding natural language.
Notable Models
Several notable models have emerged in recent years, each with its strengths and applications. GPT (Generative Pre-trained Transformer) [28] is a popular model designed mainly for text generation. BERT (Bidirectional Encoder Representations from Transformers) [10] excels in understanding context by generating contextualized representations of words that can be fine-tuned for various NLP tasks [35]. These models have achieved state-of-the-art results on a wide range of NLP tasks, demonstrating the effectiveness of LLMs.
Evaluation Methodology
To compare our work with NLP2REST, a previous study on RESTful services for NLP tasks [17], we collect nine RESTful services related to NLP. Having access to a ground truth table of extracted rules in NLP2REST, we can easily evaluate our work and identify areas for improvement [35]. Our evaluation methodology focuses on comparing the performance of LLMs on various NLP tasks with those of NLP2REST.
Conclusion
Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP). These models have been successful in capturing intricate linguistic nuances and semantic contexts, making them adept at various tasks such as text generation and answering questions. In this article, we explored the architecture behind LLMs, notable models, and evaluation methodology. By providing an artifact repository for reproducibility and future works, we aim to promote further research in this exciting area of NLP. As LLMs continue to improve, their potential applications are vast, from chatbots and language translation to content creation and more. The future of NLP is bright, and LLMs are at the forefront of this revolution.
