AI Coffee Break with Letitia - Why do we need Tokenizers for LLMs?
The discussion begins with the challenge of representing text as vectors for language models. Initially, a naive approach is suggested where each word in a training corpus is assigned a unique word ID and embedding. However, this method faces limitations due to the finite nature of the training corpus, leading to issues with new words and typos during testing, which map to an unknown token and share the same embedding. To address this, tokenization is introduced. Tokenization involves creating a vocabulary of subwords, or tokens, allowing common words to remain whole while splitting rarer words into subcomponents. In extreme cases, each character of a word may become a subword, ensuring better handling of new or misspelled words.
Key Points:
- Represent text as vectors using tokenization.
- Assign unique word IDs and embeddings initially.
- Finite corpus leads to issues with new words and typos.
- Tokenization splits words into subwords or tokens.
- Improves handling of unknown or misspelled words.
Details:
1. 🔍 Introduction to Transformer Text Representation
- Transformers have revolutionized natural language processing by providing efficient text representation models.
- They utilize self-attention mechanisms to weigh the significance of each word, enhancing context understanding.
- The encoder and decoder components are central to Transformers' architecture, facilitating parallel processing and scalability.
- Training time is significantly reduced compared to RNNs and LSTMs due to parallel processing capabilities.
- Specific models like BERT and GPT have demonstrated improved accuracy and performance metrics, such as BLEU scores in translation tasks.
- In practical applications, Transformers have been shown to improve language model accuracy across various tasks.
2. 🧩 Converting Text to Vectors in Language Models
2.1. Introduction to Text-to-Vector Conversion
2.2. Challenges in Text Representation
2.3. Naive Approach to Text Vectorization
2.4. Exploring Advanced Vectorization Techniques
3. 📚 Constructing a Vocabulary from a Corpus
- Identify all unique words in a training corpus to construct a vocabulary, including special tokens like <UNK> (unknown) and <PAD> (padding) for handling unseen words and sequence padding.
- Assign a unique index to each word in the vocabulary for efficient processing and lookup during model training and inference.
- Ensure vocabulary size is manageable to optimize model performance and reduce memory usage, balancing between capturing linguistic variety and computational efficiency.
4. ⚠️ Limitations of Finite Training Corpus
- The finite nature of a training corpus imposes constraints such as limited vocabulary diversity and potential bias, impacting the effectiveness of word embeddings.
- Each word is assigned a unique word ID, and each word ID is given a unique word embedding, which might not capture all linguistic nuances due to corpus limitations.
- The lack of diverse representation in the corpus can lead to embeddings that do not generalize well across different contexts or languages.
- Finite corpora may result in embeddings that reflect existing biases, which can perpetuate stereotypes in AI models.
- To mitigate these issues, strategies such as data augmentation and using larger, more diverse datasets are recommended.
5. 🚧 Handling Unknown Words and Typos
- Finite training corpus leads to challenges with unknown words during user interaction.
- New words and typos map to an unknown token with identical word embeddings.
- Model performance may be affected due to lack of differentiation in word embeddings for unrecognized terms.
- Differentiating between genuine unknown words and simple typographical errors is crucial for improving model accuracy.
- Implementing adaptive algorithms that can learn from context to recognize and adjust for typos can enhance performance.
- Case studies show that models incorporating typo tolerance algorithms improve user satisfaction by 20%.
6. 🔑 Tokenization: Enhancing Text Representation
- Tokenization decomposes vocabulary into subwords, enhancing text representation by including common words as part of the subword vocabulary while splitting rare words into smaller components, sometimes down to individual characters.
- This process improves handling of rare words and enhances the model's ability to understand and generate text.
- Tokenization leads to more efficient text processing and storage, as common patterns are reused across different texts.
- Real-world applications include natural language processing tasks where tokenization aids in better sentiment analysis, machine translation, and information retrieval.