How to train your dragon. Unravelling the Training of Large Language Models
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The Transformer architecture has been a fundamental lever of change in natural language processing. In the article we introduce the concept of “pre-training”, where models acquire general language knowledge before being tuned for specific tasks, significantly improving their performance.
We also discuss the potential of these models in a variety of applications, from creative content generation to sophisticated virtual assistants. We point to some challenges and controversies. The need for large datasets raises concerns about the carbon footprint and ethical issues, such as the intellectual property of training data or the potential of this technology for misinformation. The ethical and social responsibility associated with these advances in artificial intelligence must be given due weight.
At LHF Labs we believe that the future of large language model training will continue to be a crucial field, with the potential to change our interaction with artificial intelligence and, by extension, the world around us.
Training large language models
In the last year or year and a half, large language models have leapt into the space of the everyday and have become a topic of conversation, material for memes and also a cause for concern for some people, generating a lot of buzz around them.
Beyond the anecdote and the controversy, what is certain is that they have an enormous potential to transform our relations with technology, which is nothing more than the materialisation of our knowledge of the world in instruments that allow us to intervene in it.
In this case, our understanding of automatic natural language processing has allowed us to create systems capable of generating fluid text almost magically. In fact, behind this lies an enormously complex web of computations and algorithms for building language models.
Transformer Architecture: The Pillar of Success
At the heart of these models is the Transformer architecture, an innovation that has proven to be crucial for natural language processing as we told you the other day. Unlike previous architectures, such as recurrent networks or n-grams, Transformers allow you to capture relationships more effectively, which is essential for understanding human language in context.
Pre-tuning training: A Paradigm Shift
One thing that has changed the way things are done, which has greatly increased efficiency, is pre-training. Basically, instead of starting the training of a language model from scratch for each specific task, models are pre-trained on large amounts of unlabeled data. This pre-training process allows the model to acquire a general knowledge of the language and to capture complex linguistic patterns and structures. These pre-trained models are then tuned for specific tasks, which greatly improves their performance. For example, in the case of OpenAI models, the pre-trained model would be GPT-3 and the tuned model ChatGPT.
Challenges and Controversies
To achieve good results, it is essential to pre-train models with huge, vast, gigantic collections of data. This necessity opens the door to several legitimate criticisms concerning the carbon footprint of the required computing power, accessibility of data, intellectual property, etc.
In addition, the models generate very persuasive texts that can sometimes be erroneous or invented (hallucinations). They could even be used to generate malicious content in large quantities, which is not a trivial issue in a context where misinformation is part of everyday life.
Applications and the Future
Large language models have an enormous field of application, and it remains to be seen what kind of applications we can imagine with them. They are already being used to generate creative content or in increasingly sophisticated virtual assistants. It remains to be seen what the convenience between these models and people will look like, and what use we will make of the technology. It is a challenge to achieve a sustainable model in which technology is truly at the service of people and not just a small group of economic agents, not to mention how it will affect the labour market.
On the other hand, technology promises very exciting applications that could be used to make our lives better and allocate resources to the tasks that add the most value to human activity.
Beyond Words
The training of large language models has ushered in an exciting stage in the development of artificial intelligence. It is not simply a matter of processing words; it is the ability to understand, reason and generate content that is very difficult to distinguish from human in terms of language.
However, with this capability comes the responsibility to address the ethical and social challenges that accompany this progress. As we move into the future, the training of large language models will continue to be a crucial field of study, offering breakthroughs that will change the way we interact with artificial intelligence and, by extension, the world around us.
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Want to know more?
Here is a list of links to articles that are fundamental to delving into the great language models. They are fundamental in the sense that they laid the foundations, so we put them here even though they are a few years old.
- “Attention is All You Need“ – Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017).
This work introduced the Transformer architecture, which has been instrumental in the development of modern language models. - “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding“ – Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018).
BERT (Bidirectional Encoder Representations from Transformers) is another influential model that has contributed significantly to natural language processing. - “Language Models are Few-Shot Learners” – Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020).
This paper presents GPT-3 and highlights the ability of language models to perform diverse tasks with minimal input information. - “Scaling Laws for Neural Language Models“ – Kaplan, J., McCandlish, S., Henighan, T., Brown, T., Chess, B., Child, R., … & Amodei, D. (2020).
Offers insight into the scale of language models and associated computational challenges. - “Taxonomy of Risks posed by Language Models” – Weidinger, Laura, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, et al. 2022.
A classification of possible risks associated with the use of large language models. - “Gradient-Based Learning Applied to Document Recognition” – LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998).
Although not specifically focused on language models, this work is fundamental in the development of deep learning algorithms, which are essential for the computation of language models. - “A Comprehensive Overview of Large Language Models.” – Naveed, Humza, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. (2023).
This article provides a concise overview of the existing literature on LLM-related concepts, discussing background concepts and advanced topics. It serves as a systematic survey and reference.
