How LLM Works and How LLM is built

Large Language Models (LLMs) have evolved into one of the most sought-after areas of AI lately. These models are on their way to being implemented everywhere. More and more organizations pursue the idea of creating software that can understand language.

LLMs provide new possibilities for the exploration of human language and cognitive research. Its expansion has led to the extended utilization of neural networks for natural language processing tasks, which in turn are widespread in various spheres of business. Let's explore in more detail what LLMs are and how they operate.

What is a large language model?

Large Language Models are machine learning models that employ Artificial Neural Network and large data repositories to power Natural Language Processing (NLP) applications. An LLM serves as a type of AI model designed to be able to grasp, create and manipulate natural language.

These models rely on deep learning technologies, like neural networks, to perform word processing and analysis of textual information. Large language models are taught on immense amounts of text training data to memorize the patterns and grammar of a language.

Language models are called large due to the fact that they represent large-scale systems, in addition to the massive training dataset. In fact, it is so large that cannot be launched on a single machine. That's why usually it is accessible through a web interface or API.

Large language models are suitable for solving multi-faceted tasks practically for any textual query of the user, as opposed to being trained for one specific task. By studying data such as books, documents, and web pages, they can understand the intricacies of any language. Thus, they can devise content and generate text in multiple languages indistinguishable from human writing.

One such prevalent large language model at the moment is OpenAI's GPT (Generative Pre-trained Transformer). GPT-3 stands as one of the world's major artificial intelligence models, which can perform tasks such as qualitatively generating lengthy articles and replying to various inquiries, and even writing software code.

How large language models work

Large language models are made up of a neural network with tens of millions to hundreds of billions of parameters. Typically, the more parameters a neural network possesses, the better it consolidates its skills and knowledge. Such neural networks exist within advanced AI assistants, allowing us to communicate with the machine. So, how does a large language model work? The LLMs are introduced to available textual data in the preparation phase to explore the overall structure and rules of the language. The massive datasets are then submitted to a model referred to as a transformer during a training process. Transformer is a type of deep-learning algorithm.

The core notion of a language model consists of its capacity to predict the next word, called a token, based on the pre-existing text. A family of machine learning architectures called transformer architecture has turned this process into a more human-like one.

The neural network of transformer models comprised of two sets of layers, which in turn have multiple layers too:

• Encoder, which extracts relevant pieces of information from an incoming data sequence;

• Decoder, which utilizes the retrieved data to generate the output sequence components.

The encoder receives a set of tokens as input. It may be a single word, punctuation mark, or sequence of characters. The encoder then retrieves the value of the input data and stores it as a vector (hidden representation of the model input). The decoder then receives this vector and generates its interpretation of the input text.

The transformer model mixes the option of model pre-training, parallel data processing, and extensive application of the attention mechanism. Pre-training implies that a machine learning model is first trained on a large set of text data, and then a process of fine-tuning may be applied to it to address a specific problem. Fine-tuning helps to enhance a pre-trained model that already has some knowledge, with minor adjustments, without having to teach it from the beginning.

The attention mechanism lets the transformer model specifically focus on the crucial parts of the text or the most essential words in a sentence. The neural network analyzes which position of the incoming sequence is important for the specific position of the sequence in the output.

Among other things, the transformer model makes it possible to process all input text simultaneously or in parallel, rather than in sequence. This makes it possible for transformers to learn from huge amounts of data and significantly reduce training time compared to other methods.

Transformers can handle several excerpts of long texts at the same time. The model does not neglect the beginning of the text but rather makes use of what has already been studied before, builds better connections between words, and can make sense by understanding the context of a considerable amount of data.

Training data for large language models

A large language model demands large amounts of text data for model training, thus ensuring that contextually relevant responses are provided. The large language model training process may involve any kind of text data, and there is no need for any sort of labeling of this data beforehand. However, at this stage human input is needed to collect and clean datasets.

The outcome of training is the language model successfully predicting the next word based on information about the words that come before it. Therefore, absolutely any text written by a human will do. Any book, commentary, or essay is already a ready-made training piece of data, as it already consists of a tremendous array of "word-next word" type sequences.

Applications for businesses

Large language models can enhance the productivity of a business in lots of ways. Their power to interpret human queries and resolve relatively complex problems helps us put routine, time-consuming tasks in the hands of a chatbot and then simply verify the outcomes.

Pre-trained transformer models may be designed to quickly fulfill the appropriate goals of your business. They already possess the knowledge required and deeply comprehend the target language, enabling you to focus selectively on fine-tuning the model for any specific tasks you have in mind. Here are some LLM applications:

Sales and customer service

Chatbots and AI assistants custom-trained for your business deliver high-quality, prompt feedback and support to your customers. The sales bot can interact with your customers, present the range of products, inform them about discounts and promotions as well as motivate them to make a purchase.

Generation of Content

Human-like text generation is one of the key features of language models. AI creates texts that assist your products to stand out among competitors. Additionally, they can acquire the ability to generate documentation based on your knowledge base which can significantly speed up your company's document workflow.

Content generalization

Quite often, especially in large companies, a huge amount of internal documents and various text materials are piled up in random order. It would be more convenient for employees to interact with them if they were sorted into categories. LMMs can also help with categorizing such documents.

Content moderation

LLMs assist in identifying spam, profanity, and toxic content on your resources according to the set guidelines. Large models shield users from any type of controversial content that might be considered unsafe or inappropriate, potentially tainting the online reputation of the platform.

Assistance with coding and code generation

With the LLM, developers can create code generation models. By classifying code parts in popular programming languages, they can train such a model.

How to build a large language model for your business?

Even though data labeling is not required for pre-training, to handle a specific task well, language models need fine-tuning. If you want to develop your LLM, you will require high-quality labeled data[AB3] . This calls for a certain amount of labeled data, which is most likely considerably smaller than the data originally employed to train the initial LLM.

Large language models perform well on generic tasks due to the fact that they are pre-trained on immense quantities of unlabeled text data, such as books, online commentaries, or massive document databases. To create successful applications that perform specific tasks, you require human-labeled data.

Aside from providing human-labeled data for the language model development process crowdsourcing platforms such as Toloka enable their users to automate the fine-tuning of the models. This allows them to launch their AI application without the need to hire a team of experts, but rather outsourcing it. [AB4] The LLMs are laborious in their development and upkeep, putting them out of reach for most companies. A perfect solution that makes LLMs more accessible for any enterprise is crowdsourcing.

In addition, Toloka offers the following crowdsourcing services:

• Data collection;

• Dataset cleanup;

• Prompts and instructions creation;

• Prompts moderation, categorization, validation, or feedback;

• Reinforcement learning from human feedback (RLHF) workflows

• Assessing the quality of a model output

• Moderation of model output.

Human Input in LLM Development

What is the significance of human input in the creation of LLMs? As pointed out earlier, building large language models for particular purposes demands precisely labeled data so that the language model is capable of producing reliable estimates. People can provide finer and more refined labels than computers can.

Human judgment of the model's performance is extremely critical to bring the model in line with expectations. With human labeling, the input data is consistent and taken from real-life context. Toloka сrowdsourcing platform provides human input into the development of your LLM at every stage: from collecting and labeling the necessary data to the human evaluation of model quality and moderation of model output.

Conclusion

LMM systems now can generate coherent responses to inquiries and apply the knowledge gained to produce text. Custom-trained chatbots and natural language processing applications rely on LLMs to fulfill user requests. However, they are not only required for teaching AIs human languages but also for understanding proteins, software development, and coding.

To create an effective LLM of your own, you will require a significant amount of labeled data to fine-tune a pre-trained large model. The solutions of the Toloka crowdsourcing platform are perfectly designed for this purpose, helping to bring in human input throughout the entire project of developing an LLM specifically for your company.