The 2020s have brought significant and rapid development in AI. Its widespread adoption has forced certain industries (e.g., marketing) to completely rethink their operations. The surge in AI’s popularity is largely due to increasingly advanced language models. What are they, and is it worth investing in your own? What is a language model?
A language model is a statistical mathematical model that predicts the probability of a sequence of words occurring in a given language or text. These models are used in various fields, such as natural language processing, machine translation, speech recognition, text generation, and many others. Other common applications include spell checking, word suggestions while typing, and automatic text completion. Language models use training data in the form of word or sentence sequences to learn patterns within a language. This allows the model to predict the probability of a word or sentence following a previous one. If it has learned that in the sentence “we ate dinner at home,” the word “at” follows “dinner,” it will be able to predict that the sentence “we ate dinner at home” is more probable than others. There are many types of language models. One of the most well-known is the GPT (Generative Pre-trained Transformer) model – many people may associate it with OpenAI’s ChatGPT, which revolutionized not only the IT world in early 2023 but also the operating models of industries like marketing and graphic design. GPT is a transformer model. It was trained in a generative mode, meaning it was taught to generate text based on large amounts of training data. It is widely used for text generation and machine translation.
Types of language models
Other language models include:
- Markov model – a basic language model. It is based on the assumption that the probability of a word occurring depends on the previous word in the text.
- N-Gram model – based on sequences of N consecutive words in a text, known as N-grams, which are used to calculate the probability of the next word occurring.
- Recurrent Neural Network (RNN) – can be trained on text sequences and remembers internal states, allowing it to process context-dependent sequences.
- LSTM (Long Short-Term Memory) model – a type of RNN that uses gates to control the flow of information in the network, enabling it to learn long-term dependencies in text.
- Transformer model – uses transformer architecture for learning and generating text.
- BERT model – used for natural language processing and based on a transformer. Unlike other models, it is trained on supervised machine learning tasks.
- XLNet model – based on a transformer that can consider context in both directions, meaning it takes into account both the preceding and following words in the text.
How to have your own language model?
Implementing your own language model can bring many benefits to virtually any organization. However, before starting, it is worth developing a plan where the company clearly defines its business goals, problems to be solved, and potential benefits (e.g., language models can increase business process efficiency). You must consider the company’s specifics – although language models can benefit almost any organization, their application in
e-commerce may involve different factors than in a manufacturing company. In the next stage, you need to collect a large amount of text data in the target language. This is also the moment to choose the architecture and training method for the language model. For simple models (Markov or N-Gram), you can use techniques such as maximum likelihood estimation (
MLE) or
Bayesian estimation. In turn, recurrent and transformer models may require advanced machine learning techniques, such as neural networks. Once you have a trained language model, it can be used for tasks like text generation or machine translation, but it can also be customized for other, more specific tasks. You can use ready-made libraries and tools like TensorFlow or PyTorch for training. After training, the model must be tested and optimized. The next steps are implementation and integration with other company systems and tools. It is worth monitoring performance and updating the language models as needed, although they may perform better over time as the amount of data grows.
How much does it cost to maintain and train a language model?
The costs of maintaining and training a language model depend on factors such as the type of model, the size of the training data, hardware, and infrastructure. Training costs can be particularly high, especially for large models like GPT. However, it is worth noting that technological advancements are driving training costs down. According to a report by
ARK Invest, the cost of training GPT-3 in 2020 was $4.6 million, but by 2022, the cost of training a similar model would have been only $450,000. For simpler models, such as N-Gram or Markov models, the amounts can be even lower. The cost of maintaining a language model depends on how it is used. For cloud-based NLP services, fees are charged based on the number of API requests or processed text. Conversely, if you host and deploy the model yourself, costs may be higher due to the need to maintain your own infrastructure and ensure adequate computing resources.
Can a language model be outsourced?
Like many other IT services, the training and maintenance of language models can be outsourced. This can be beneficial for companies without sufficient knowledge or resources to implement their own language model. Outsourcing can cover the entire project or just specific parts of it. It may include model training, maintenance, updates, or technical support. Outsourcing costs depend on the size and complexity of the model, project requirements, and available financial resources. This does not change the fact that outsourcing can lead to savings in time and money. Regardless of whether an organization decides to implement it internally or hire subcontractors, a proprietary language model can significantly improve efficiency and influence its market position.
