What is a Large Language Model (LLM)?

A Large Language Model (LLM) is a type of AI system that uses massive amounts of text data to learn about and manipulate human language by generating natural-sounding text or responding to prompts. LLMs typically have deep learning architectures, often with deep transformers, and can understand relationships between words, phrases, and meanings. 

LLMs are called “large” because of their amount of input text (trillions of words) and due to their number of parameters (many hundreds of billions). Examples of LLMs you may have heard of include OpenAI’s GPT family, Google’s PaLM, and Meta’s LLaMA models.

How do LLMs work?

LLMs function by predicting the next most likely word (or token) in a sequence based on the words that came before it. LLMs can learn from enormous text corpora through the process of self-supervised learning and learn to model the statistical patterns of the language without any labels. 

Most LLMs are built on a transformer. Transformers are an architecture that uses paths and mechanisms like self-attention to look at relationships between words through long sequences of text. Every set of input words is passed through several layers of processing. The transformer builds internal representations that allow it to predict context and semantics, and even style. When using LMs, the transformer applies all of this knowledge to create responses, summaries, code snippets, translations, etc.

What are the key capabilities of LLMs?

LLMs are versatile tools with a wide range of applications across industries. Their primary capabilities include:

• Text Generation: Producing human-like text for articles, stories, or dialogue.
• Summarization: Condensing long documents into shorter, accurate summaries.
• Question Answering: Responding to queries using both context and general knowledge.
• Translation: Converting text between languages with high fluency.
• Code Generation: Writing or completing code snippets across multiple programming languages.
• Semantic Understanding: Analyzing sentiment, intent, or meaning behind text.
• Content Transformation: Reformatting or rewriting text for different audiences or tones.

What are the limitations of LLMs?

Despite their strengths, LLMs also face notable constraints that organizations and users must account for:

• Hallucinations: The model may generate confident but factually incorrect information.
• Bias: Training data can embed social or cultural biases that surface in outputs.
• Lack of True Understanding: LLMs recognize patterns but do not possess genuine reasoning or comprehension.
• Context Window Limits: Models can only process a fixed amount of text at once, restricting long-context tasks.
• Resource Demands: Training and deploying large models require massive computational power and energy.
• Data Privacy Risks: If not carefully managed, models may expose or leak confidential information present in training data.
• Limited Domain Expertise: Without fine-tuning, LLMs may struggle in highly specialized or technical fields.