Natural Language Processing in the Age of Foundation Models: From BERT to GPT-5
Volume 1 Issue 1
Year of Publication : 2025
Authors : Shenroy Varatharaj, Kamal Viswanathan, Arunai Ganesan, Dr. Siva Sankari
Doi : XXXX XXXX XXXX
Keywords
NLP, BERT, GPT-5, Transformer, Foundation Models, Large Language Models, Deep Learning, Self-supervised Learning.
Abstract
Natural Language Processing (NLP) has transformed from systems relying on handcrafted linguistic knowledge and co-occurrences, to statistical approaches with feature-based pattern matching, and recently into a paradigm shift based on foundations models—large scale pre-trained architectures learning representations over large amounts of data in an unsupervised way expected to capture general properties of the language across task and domain. Those transformation has revolutionalized the nature of machines understanding, representing and generation human language which was firstly infiringed by Transformer architecture. The pre-trained models: BERT, GPT-3, T5, PaLM and GPT-5 are the giant leap in computational linguistics. They replace narrow-eyed learning with one-shot universal linguistic understanding as are reasoning machines!
This paper conducts a thorough investigation on the evolution of NLP in the era of foundation models, highlighting both advancement in technology and philosophical reflection behind it. It studies how the bidirectional pretraining of BERT brought deep contextual understanding and how later autoregressive models such as GPT-3 and GPT-4 evolved it into creative and reasoning abilities. architectures Agentic AI9 The shift toward GPT-5 suggests a new mental model for agentic AI systems, which might be defined more directly in terms of the ability to perform high-level reasoning and metalearning, using internal goal decompositionalgorithms, planning algorithms,andhownovel agents learn about their own reasoning processes through meta-learning or feedback loops based on its own actions.
Moreover, we try to provide some understanding the core architecture of foundation models (e.g., self-attention), along with scaling laws by exploring pretraining objectives and fine-tuning methods including RLHF and Constitutional AI. The work compares the most relevant models in terms of number of parameters, diversity of data, modality and general downstream. It demonstrates that NLP and multimodal intelligence are increasingly joining forces, as modern models unify text, vision and audio processing under one roof to approximate general cognitive reasoning.
Much of this work involves applying foundation models in practice — including machine translation, summarization, sentiment analysis, code generation, education tutoring and healthcare communication. These features further reveal how NLP models are emerging as cognitive artifacts that augment human abilities to discover and communicate knowledge.
At the same time, the paper addresses important ethical, interpretability and environmental issues related to training and using such large models. Problems such as biased data, hallucination, invasion of privacy and high power consumption suggest that responsible regulation and sustainable strategies for AI are needed. We focus on the recent trends such as accurate/efficient fine-tuning, model compression and XAI (Explainable AI) techniques to make models more transparency/fairness.
In the end, I believe that it reminds us that even when expanded to BERT-based and further GPT-5-like systems, the history of NLP is one leading towards foundation-driven AI: language models don't just help people analyze and generate language but may become fully autonomous agents capable of reasoning or acting in a world. The paper claims that in the next phase of NLP research, we must pay special attention to the generalization and interpretability of these models when evolving foundation models—balancing model size with transparency and control—and advocate for a more rigorous treatment of actual as well as potential risks: ethical, systemic or emergent which pertain not only to biases but also to ecological impacts such models may have in their environments.
Cite this Article
Shenroy Varatharaj, Kamal Viswanathan, Arunai Ganesan, Dr. Siva Sankari, 2025. "Natural Language Processing in the Age of Foundation Models: From BERT to GPT-5", International Journal for Research in Computer Science and Information Technology (IJRCSIT) 1(1): 1-19.