In this Chapter, we will delve into the crucial process of preparing text data for Large Language Models (LLMs). Our focus will be on transforming raw text into a format that these models can effectively use for training. This involves several steps, including tokenization, encoding, and creating training pairs. By the end of this chapter, you’ll have a thorough understanding of these processes and be well-equipped to prepare data for training powerful LLMs.
EP 1: Data Wrangling for LLMs – Taming the Text Beast!
In the previous episode, we introduced the mighty LLMs. Now, it’s time to prep their food – the training data. Imagine a hungry LLM. We can’t throw a whole book at it! We need bite-sized pieces, like words or subwords (tokens). We’ll learn to chop text into these tokens, making them easier to digest for the LLM.
But words aren’t enough! We need to translate them into a special code (encoding) the LLM understands. Think of it like assigning secret ingredient codes for the LLM chef! Popular LLMs use a sophisticated technique called Byte Pair Encoding (BPE) to handle even the trickiest words. It’s like having a super-sharp knife to break down any ingredient!
Finally, we’ll create training pairs: a sequence of tokens followed by the next predicted token. It’s like a recipe – each step leads to the next ingredient!
By the end of this series, you’ll be a data-wrangling champion, preparing the perfect training feast for our LLMs! Get ready to learn tokenization, encoding, and building training pairs – the key ingredients for building powerful LLMs in the next episode!
Tokenization: Breaking Down Text
Tokenization is the process of breaking down text into smaller units called tokens. These tokens can be words, subwords, or even individual characters. Tokenization is crucial because LLMs operate on these tokens rather than raw text.
- Words vs. Subwords: While tokenizing text into words is straightforward, it can be inefficient, especially for languages with a rich morphology. Subword tokenization, such as Byte Pair Encoding (BPE), provides a more granular approach, breaking words into smaller units that are easier for the model to handle.
- Byte Pair Encoding (BPE): BPE is a popular subword tokenization technique. It iteratively merges the most frequent pairs of characters or subwords in the text. This approach helps handle rare words and morphological variations effectively.
Encoding: Translating Tokens to Numbers
Once we have our tokens, the next step is to convert them into a format that the LLM can understand – numbers. This process is known as encoding.
- Word Embeddings: Word embeddings are dense vector representations of tokens. They capture the semantic meaning of words and their relationships with other words. Popular methods for creating word embeddings include Word2Vec and GloVe.
- Word2Vec: This model predicts a word’s context based on its neighbors. Words with similar contexts (like „king“ and „queen“) receive similar codes, clustering together in the embedding space.
Embedding Size
The dimensionality of word embeddings can vary. Higher dimensions capture more complex relationships but require more computational resources. For instance, GPT-3 uses embeddings with thousands of dimensions, allowing it to understand subtle nuances in language.
Creating Training Pairs
After encoding the text, the next step is to create training pairs. This involves taking a sequence of tokens and predicting the next token in the sequence. These training pairs are used to teach the LLM how to generate text.
- Sequence-to-Sequence: Each training pair consists of an input sequence (a series of tokens) and a target token (the next token in the sequence). This sequence-to-sequence learning enables the model to understand and generate coherent text.
Decoding Word Soup
We’ve chopped our text into tokens, but LLMs can’t understand words directly. They need numbers! That’s where word embeddings come in. Imagine a secret codebook for the LLM chef. Each word is assigned a unique code (vector) – like a recipe key. This lets the LLM understand the meaning and relationships between words.
- Word2Vec and Context: Word2Vec predicts a word’s context based on its neighbors. This method ensures that words with similar contexts (like „king“ and „queen“) get similar codes, allowing them to cluster together in the embedding space.
In the next section, we’ll explore how to prepare these embeddings for LLMs. We’ll learn how to split text, convert words to tokens, and finally, turn those tokens into the special code the LLM understands – its secret recipe for understanding language!
Conclusion
In this chapter, we covered the essential steps for preparing text data for LLMs. From tokenization to encoding and creating training pairs, each step plays a vital role in ensuring that the model can effectively learn from the data. By mastering these techniques, you’re well on your way to becoming an expert in data wrangling for LLMs.
