Introduction to Large Language Models

Overview of language models

Language models are machine learning models with the ability to understand, process, and generate human-like text. These models are trained on existing human-written text that allows them to predict a series of words in a string of text. One common example of such a language model is the autocomplete functionality in our everyday editor, whether on mobile messaging applications or search platforms.

The initial language models were mainly rule based. These models rely on predefined linguistic rules to process and generate text, restricting them from understanding the complexity of context in human language. With the advancement in computational power and the introduction of transformer architecture, statistical language models are able to predict the likelihood of words and phrases. More importantly, these statistical models are now able to learn the contextualized representations of words that play a crucial role in bridging the gap between human communication and machine understanding. Interpreting user queries or engaging in meaningful conversations with humans is only possible because of language models that enhance human-computer interactions.

What are large language models?

Large language models (LLMs) are the scaled-up version of the statistical language models trained with billions of parameters over a large corpus of text dataset. This combined with the transformer architecture makes them capable of transforming the knowledge gained from one task and applying it to another. This property has revolutionized the use of language models across vast applications because it fundamentally alters how these models are employed. Traditionally, a model needs to be trained for every specific task. However, LLMs can leverage their preexisting knowledge to adapt swiftly to new tasks with minimal fine-tuning.

Why LLMs?

LLMs have gained widespread adoption due to their ability to perform language understanding and generation tasks like sentiment analysis, text summarization, language translation, question-answering, or rephrasing. Several LLMs have emerged in recent years, including generative pre-trained transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT).

Let’s look at the key reason why LLMs are preferred in natural language processing (NLP) tasks:

• Contextual understanding: LLMs are trained on a large corpus of texts and can capture the contextual information in language. LLMs consider the entire sentence or paragraph to get a deeper understanding and are therefore able to respond with contextually relevant and accurate results.

• Semantic richness: LLMs use contextual embeddings during training that help understand the meaning of a word based on its surroundings. This is particularly useful in tasks like summarization, where the model needs to generate a concise summary while retaining the original meaning.

• Multilingual competence: LLMs are trained on multilingual datasets to understand the diverse linguistic context. This also allows LLMs to understand similar concepts in different languages and effectively perform tasks like language translation.

• Handling ambiguity: LLMs can effectively handle ambiguity in human language by considering a broader context of words and phrases in different languages.

• Adaptability: One main feature of LLMs is that they can transform learning from one task to another. This makes them a very powerful tool where pretraining on task-specific training data is not needed anymore.

• Reduced dependency on handcrafted features: Traditional machine learning models require feature engineering, where expert knowledge is critical in the training phase. Whereas LLMs automatically learn the relevant features from the data due to their capability for contextual understanding.