747: Technical Intro to Transformers and LLMs, with Kirill Eremenko
Jan 9, 2024
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Data scientist Kirill Eremenko discusses the basics of transformers and LLMs, emphasizing the five building blocks of transformer architecture and why transformers are so powerful. Topics include AI recruitment, a new course on LLMs, and the impact of LLMs on data science jobs.
Transformers utilize the attention mechanism for semantic and contextual understanding.
LLMs have five stages of data processing, enhancing text generation tasks.
Positional encoding is vital for preserving word order and contextual significance.
Transformers distinguish between entities by employing Q, K, V vectors.
Training and inference in LLMs involve multiple data segments and parallelization.
Deep dives
Floor 1: Input Embeddings
In creating input embeddings, each word is converted into unique vectors that capture semantic meaning. These vectors are then enriched with contextual meaning through positional encoding, allowing for the preservation of word order and contextual significance in the sentence.
Floor 2: Positional Encoding
Positional encoding is crucial for maintaining word order and contextual meaning within a sentence. By adding positional information to the embedded vectors, the transformer model can understand the significance of the position of words within the sentence.
Floor 3: Attention Mechanism
The attention mechanism is the heart of the transformer model, enabling it to capture contextual meaning in addition to semantic understanding. This mechanism involves creating Q, K, and V vectors for each word, where the Q vector represents what the word is interested in, the K vector indexes the information of the V vector, and the V vector contains the value or concept of interest.
Floor 4: Conceptualizing Transformers
Transformers employ the attention mechanism to enrich vectors with both semantic and contextual meaning, allowing words to effectively communicate their significance and relation to other words in the sentence. Through Q, K, and V vectors, each word is able to convey its specific query, key, and value, enhancing the model's understanding of text.
The Dynamics of Text Generation
The comprehensive approach of transformers in handling semantic and contextual meaning transforms text generation tasks, enabling the model to differentiate between entities refered to by pronouns like 'it,' depending on the context. By employing Q, K, and V vectors, the transformer model navigates the nuances of language, ensuring accurate and coherent text generation.
Understanding Transformers and Attention in LLMs
Transformers and large language models (LLMs) are powered by attention mechanisms that allow for processing data in five stages: input embedding, positional encoding, the attention mechanism, a feed-forward neural network, and linear transformation with softmax. Encoders like BERT excel in natural language understanding, while decoders like GPT models are ideal for generative tasks.
LLMs for Training and Inference
LLMs are used for both training and inference. During training, segments of data are fed into the transformer for error calculation and weight adjustments. The parallelization within segments is a key feature, allowing multiple predictions simultaneously. Inference involves predicting the next token in the sequence, with the ability to generate text based on learned patterns.
Enhancing Psychological Understanding
'The Big Leap' by Gay Hendricks explores operating in zones of incompetence, competence, expertise, and genius. It delves into the thermostat principle, illustrating how humans revert to their comfort zones in various aspects of life. Recognizing and enhancing psychological barriers is essential for personal development and success.
Bonus Trivia on Transformers and Attention
Transformers feature multiple attention heads and layers to capture complex relationships across data. The GPT model includes 96 attention heads in each of the 96 layers, enhancing learning capabilities. By expanding attention mechanisms, transformers achieve significant depth and breadth in processing language data.
The Future of Large Language Models
The rise of large language models reflects a shift towards non-reasoning intelligence, where predicting the next word drives human-like behavior and creativity. Understanding the psychology behind success, the thermostat principle, and the comfort of familiar zones is key to navigating the evolving landscape of AI technology.
Attention and transformers in LLMs, the five stages of data processing, and a brand-new Large Language Models A-Z course: Kirill Eremenko joins host Jon Krohn to explore what goes into well-crafted LLMs, what makes Transformers so powerful, and how to succeed as a data scientist in this new age of generative AI.
In this episode you will learn: • Supply and demand in AI recruitment [08:30] • Kirill and Hadelin's new course on LLMs, “Large Language Models (LLMs), Transformers & GPT A-Z” [15:37] • The learning difficulty in understanding LLMs [19:46] • The basics of LLMs [22:00] • The five building blocks of transformer architecture [36:29] - 1: Input embedding [44:10] - 2: Positional encoding [50:46] - 3: Attention mechanism [54:04] - 4: Feedforward neural network [1:16:17] - 5: Linear transformation and softmax [1:19:16] • Inference vs training time [1:29:12] • Why transformers are so powerful [1:49:22]
