#13 Let's Prepare for the Machine Learning Interview: LLM's

Large language models use transformer models and are trained using massive datasets — hence, large. This enables them to recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets.

Key components of large language models

Large language models are built on several layers:

• The embedding layer: Creates embeddings from the input text. This part captures the semantic and syntactic meaning of the input, enabling the model to understand context.
• The feedforward layer (FFN): Composed of several fully connected layers that transform the input embeddings to derive higher-level abstractions and understand user intent.
• The recurrent layer: Interprets words in sequence and captures the relationship between words in a sentence.
• The attention mechanism: Enables the model to focus on specific parts of the input text relevant to the current task for more accurate outputs.

What is the difference between large language models and generative AI?

Generative AI is an umbrella term for AI models capable of generating content (text, code, images, video, music). Large language models (LLMs) are a specific type of generative AI trained on text to produce textual content. Examples include ChatGPT for LLMs, and Midjourney or DALL-E for other generative AI. All LLMs are generative AI.

How do large language models work?

LLMs are generally based on the transformer architecture and trained to predict the next token in a sequence.

• Training: Pre-trained using massive textual datasets (Wikipedia, GitHub, etc.) consisting of trillions of words through unsupervised learning.
• Fine-tuning: Optimizes the model for specific tasks (like translation) through targeted activity training.
• Prompt-tuning: Similar to fine-tuning, it trains a model via few-shot or zero-shot prompting based on specific instructions (prompts).

Limitations and challenges of large language models

• Hallucinations: When an LLM produces an output that is false or fails to match the user's intent.
• Bias: Training data reflecting single demographics or lacking diversity results in biased outputs.
• Scaling: Scaling and maintaining these models is resource-intensive and time-consuming.
• Deployment: Requires significant technical expertise in deep learning, transformer models, and distributed systems.

*This post originally appeared on my Medium

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