Have you ever heard of Large Language Models? Also known as LLMs? Soon they will replace us more and more in everyday life – because LLMs are actually what we perceive as “AI”. LLMs are artificial intelligence models that are trained to generate, understand, and respond to human-like texts. This technology enables machines to be able to interpret human language the way humans do. A well-known example of this is OpenAI’s GPT series, which is currently at GPT-4.

What are Large Language Models?

Large Language Models are artificial neural networks that aim to understand and generate human language. They are based on an architecture called Transformer, which allows them to recognize complex linguistic patterns and connections. These models can have millions or even billions of parameters that they adjust during the training process to achieve better performance.

How are these trained? With your and my data, photos from the Internet, texts, websites, books and much more – this creates a closed system, a kind of library, with which ChatGPT, for example, then “predicts” your text and does NOT (!) re-research it.

How are LLMs trained?

The training process of LLMs consists of two main phases: forward and backward propagation. Below we explain this process in detail:

Forward Propagation: During forward propagation, the model generates predictions for each word in a text based on the context of the previous words. These predictions are then compared to the actual words in the text, and the difference between the predictions and the real data is measured as a loss (medium.com).

Backpropagation: During backward propagation, the loss is propagated back through the network to adjust the parameters of the model. This process is repeated until the model achieves acceptable performance (edureka.co).

The backpropagation algorithm consists of the following steps (edureka.co):

• Initialization of network parameters (often with small random values)
• For each training example: prediction = neural-net-output(network, ex) (forward propagation)
• Calculation of the error (prediction – actual value) on the output units
• Calculation of Δw_h for all weights from the hidden layer to the output layer (backward propagation)
• Calculating Δw_i for all weights from input layer to hidden layer (backward propagation continued)
• Update network parameters (input layer is not modified by error estimation)
• Repeat until all examples are correctly classified or another stopping criterion is met

Training LLMs requires large amounts of data compiled from texts of different genres, topics and styles. An alternative method to speed up the training process of LLMs is sparse training, in which forward and backward propagation are sparsified. This can significantly speed up the training process and reduce memory consumption (arxiv.org).

Applications of LLMs

LLMs have numerous applications in various areas such as:

• Text generation: LLMs can be used to generate coherent and relevant texts in different styles, such as news articles, blog posts, or literary works. Machine translation: They can translate between different languages in real time, taking into account cultural nuances and context.
• Sentiment analysis: LLMs can recognize and categorize the emotions and opinions in texts.
• Text Summary: You can automatically convert long texts into shorter, succinct summaries.
• Image, audio and video generation: with text-to-X models you can already create multimedia content… and there are also concerns.

Limitations and ethical concerns

Despite their impressive capabilities, there are also limitations and ethical concerns associated with LLMs

1. Energy and computing costs: Training LLMs consumes enormous amounts of computing power and energy, which has both financial and environmental implications.
2. Creativity and Originality: Although LLMs are capable of producing human-like texts, they lack the ability to demonstrate true creativity or originality as they are based solely on the patterns and structures learned during training.
3. Data Protection: Because LLMs are trained on publicly available texts, there is a possibility that they may inadvertently reproduce or use confidential or personal information.

Where is the LLM journey heading?

Even if the functionality behind the Large Language Model has not yet been fully researched, you can get a rough overview of it. It is a machine learning system that can understand text in a specific language through the use of neural networks. Unlike other machine learning technologies that rely on words, the Large Language Model relies on the use of sentence structure and syntax. This means that it is able to recognize connections between words, thus providing predictable results. By using the Large Language Model, it is possible to understand and interpret text in a specific language. This makes it easier to learn and use natural language skills.

The Large Language Model therefore offers a variety of options for processing and interpreting the text in a specific language. It can be used to analyze, classify and understand text.

Examples of large language models

In recent years, large language models (LLMs) have been developed, representing a significant advance in AI development. Here are some examples of large language models:

• BLOOM: A model with around 175 billion parameters developed by an international team of around 1,000 volunteer researchers. This project was funded by the French government, US AI company Hugging Face and others and cost $7 million in computing time
• PaLM: A large language model from Google, which required around 3.4 gigawatt hours to train over a period of around two months, which is equivalent to the annual electricity consumption of around 300 US households
• GLaM: An energy-efficient language model from Google that required the same amount of computing resources as GPT-3, but only used about a third of the energy due to improvements in training software and hardwarermer) – von Google

Other LLMs include:

• GPT-3 & 4 (Generative Pretrained Transformer 3 & 4) – by OpenAI
• BERT (Bidirectional Encoder Representations from Transformers) – by Google
• RoBERTa (Robustly Optimized BERT Approach) – from Facebook
• AI. T5 (Text-to-Text Transfer Transformer) – by Google
• Megatron Turing – from NVIDIA

And if you have any questions? Use human intelligence and askRoger