Introduction
At the heart of today's digital revolution lies a technology that has transformed the way we interact with machines: transformers. These deep learning models have upended natural language processing (NLP) through their ability to understand and generate text in an almost human way. Since their introduction, transformers have paved the way for new applications, ranging from virtual assistants to machine translation systems, and they continue to push the boundaries of what is possible in the field of artificial intelligence (AI).
Transformers differ fundamentally from earlier models in their approach to attention. Rather than processing sequences linearly, they use an attention architecture that allows them to focus on different parts of the text, regardless of their position. This innovation has not only improved the accuracy of NLP tasks but has also enabled greater computational efficiency. By examining the evolution of these models, their current applications and the challenges they face, we can better understand their impact on technology and on modern society.
Background/History
The history of transformers begins in 2017 with the publication of the paper "Attention Is All You Need" by Vaswani et al. This paper introduced a new approach to language processing models, built on a multi-head attention mechanism. Before this innovation, sequence-to-sequence models such as LSTMs and GRUs were the norm, but they suffered from limitations in terms of parallelisation and efficiency.
Transformers quickly gained popularity thanks to their ability to handle large amounts of text data with unmatched accuracy. In 2018, Google introduced BERT, a transformer-based model that revolutionised natural language understanding. BERT showed that transformer models could be pre-trained on vast corpora of data and then adapted to specific tasks, a technique that has become an industry standard.
Applications/Use cases
Transformers have found applications in a multitude of fields. In the healthcare sector, for example, they are used to analyse medical records, providing more accurate diagnoses and personalised treatment recommendations. In finance, transformer models help analyse market trends and predict economic movements, enabling businesses to make better-informed decisions.
Transformers are also widely used in machine translation systems. The models of the field's leading players — GPT (OpenAI), BERT and Gemini (Google), LLaMA (Meta) or Mistral (Mistral AI) — have enabled more natural translations and contextually relevant text generation. In addition, chatbots and virtual assistants such as Alexa and Google Assistant have considerably improved their ability to understand and respond to human requests thanks to transformers.
This is precisely the field in which Koaee works: we design multilingual conversational assistants for businesses, capable of conversing in sixteen languages. These assistants rely on models from the transformer family: Mistral, including its voice model Voxtral, and OpenAI (GPT). Our servers are in Europe and no conversation is retained; response generation, however, still partly goes through OpenAI, whose processing takes place outside Europe.
Technologies/Methods
Transformers rely on an architecture made up of attention layers and feed-forward layers. Each attention layer is able to weigh the importance of each word in a sentence according to its context, which allows the model to focus on the most relevant parts of the text. This approach is what sets transformers apart from traditional NLP models.
In addition to attention, pre-training and fine-tuning techniques play a crucial role in the success of transformers. Pre-training consists of exposing the model to a vast corpus of data so that it learns linguistic structures and relationships. The model is then fine-tuned for specific tasks, such as text classification or question answering.
Where this family of models stands today
Were this article to stop at GPT-3, it would miss the essence of what has happened since: the dividing line no longer runs between laboratories, but between closed models and open-weight models, which can be downloaded and self-hosted. In August 2025, OpenAI released gpt-oss-120b and gpt-oss-20b, two open-weight models under the Apache 2.0 licence. On 2 December 2025, Mistral introduced the Mistral 3 generation, including the Large 3 model, also under Apache 2.0: an open-weight model that can be downloaded and self-hosted.
On the closed-model side, Anthropic released Claude Opus 5 on 24 July 2026. In China, Moonshot AI opened its Kimi family with K2 in July 2025, under a modified MIT licence, then evolved it up to K3 in July 2026. These models share neither the same publisher, nor the same licence, nor the same country — yet all of them descend from the architecture described in 2017 in "Attention Is All You Need". Nine years later, no one has replaced the attention mechanism: it has been made larger, faster and cheaper.
Speech has followed the same path. On 23 March 2026, Mistral released Voxtral TTS, a speech synthesis model designed for voice agents, available via API and with an open-weight version published under the CC BY-NC 4.0 licence (non-commercial) — it is this lineage that gives a voice to the assistants we deploy. For a business, the consequence is tangible: the choice is no longer limited to renting an American model. It now becomes possible to choose one's publisher, one's licence and the location where the model runs.
Challenges/Limitations
Despite their success, transformers are not without challenges. One of the main issues is their high computational cost. Training very large models, such as GPT-3 (175 billion parameters, according to the OpenAI paper published on arXiv), requires considerable hardware and energy resources, which can be prohibitive for some organisations. Furthermore, transformers tend to reproduce the biases present in their training data, which raises ethical and fairness concerns.
Efforts are under way to address these issues, notably through techniques such as model quantisation and distillation, which aim to reduce model size without sacrificing accuracy. In addition, initiatives to increase transparency and accountability in AI development are essential to ensure that these technologies are used ethically.
Outlook
The future of transformers looks promising. With the rise of multimodal models, which combine text, images and other types of data, transformers will continue to broaden their capabilities and applications. The development of more specialised models for specific industries, such as healthcare or law, will enable greater personalisation and improved accuracy in these fields.
Research also continues to focus on improving the energy efficiency of transformers, which is crucial for their large-scale adoption. As these models become more accessible and less costly to deploy, their impact on society will continue to grow, transforming the way we work, communicate and live.
Conclusion
In just a few years, transformers have revolutionised the field of natural language processing and artificial intelligence as a whole. Their ability to understand and generate text in a sophisticated way has paved the way for many innovative applications and continues to drive AI research. Despite the challenges still to be overcome, particularly in terms of bias and resources, transformers are well positioned to play a central role in the future of technology.
Sources: "Attention Is All You Need" (2017) and "BERT" (2018), original abstracts. Three measurements on different scales, placed side by side as reference points and not comparable with one another.
