The Role of Tokenizers in Building Effective Telco LLMs
Introduction
When building a Telco LLM (Large Language Model), effectively processing text data is paramount. A crucial component in this process is the 'tokenizer.' Tokenizers split text into smaller units that the model can understand, and their performance can vary significantly depending on the language. Optimized tokenizers can greatly enhance model performance. This article will delve into the basic concept of tokenizers, their importance, and how SKT’s optimized tokenizer holds a competitive edge.
What is a Tokenizer?
Let's start by understanding what a tokenizer is. A tokenizer is the first step in text data processing, breaking down sentences into smaller units called 'tokens.' For example, the sentence "SK Telecom is a leading telecommunications company in South Korea." can be tokenized into ["SK", "Telecom", "is", "a", "leading", "telecommunications", "company", "in", "South", "Korea."]. It can further be split into subwords or even individual characters.
A subword tokenizer divides words into smaller units, which is particularly useful for handling neologisms, compound words, and rarely used terms. For instance, the word "sentence" can be divided into ["sent", "ence"] by a subword tokenizer. Similarly, "monolingual" can be split into ["mono", "ling", "ual"]. This approach helps in better understanding the components of a word.
The role of a tokenizer is to convert these divided tokens into a format that the model can comprehend, significantly impacting the LLM’s performance. Beyond just splitting text, a tokenizer understands the structure and context of the language, enabling the model to grasp sentence meanings more accurately.
For example, if the word "internationalization" is tokenized as a single unit ["internationalization"], it would be treated as one long token. However, using a subword tokenizer, it can be split into smaller units like ["intern", "ation", "al", "ization"], allowing the model to better understand the word's meaning. This is especially useful for handling rare words or neologisms.
Why are Tokenizers Important?
Tokenizers are the first step in text processing, and their accuracy and efficiency directly impact the model's overall performance. Improper tokenization can lead to misinterpretation of the text, resulting in a decline in performance.
For instance, if the word "tokenizing" is incorrectly split into ["t", "okeni", "zing"], the model would struggle to understand its meaning. Thus, tokenizers are crucial for accurately processing and understanding text.
Differences in Tokenizer Performance by Language
The performance of tokenizers can vary by language due to differences in the number of tokens generated for the same meaning. An increase in the number of tokens means longer input and output sequences for the LLM, which can lengthen response times. Additionally, since LLM APIs often charge based on the length of input and output, the cost for customers can vary depending on the language.
For example, using OpenAI’s GPT-4 tokenizer, the sentence "I ate an apple" can be tokenized into 4 tokens in English: ["I", "ate", "an", "apple"]. In contrast, the equivalent Korean sentence "나는 사과를 먹었다" is tokenized into 10 tokens, and the Japanese sentence "私はリンゴを食べました" into 12 tokens.
SKT’s Tokenizer Optimization in Telco LLM
SKT has optimized its tokenizers to overcome these language-specific differences and provide optimal performance. The Telco A.X LLM from SKT features custom tokenizers tailored to various languages and contexts, outperforming competitors.
For instance, the tokenizer for complex languages like Korean is designed to consider context for more precise tokenization. This allows the model to better understand and process the meaning of text. SKT has developed its tokenizer by considering the diverse contexts and expressions in Korean during the training process. Consequently, the tokenizer can accurately capture the meaning and context of words.
Below is a comparison of SKT's tokenizer with those of other companies. Compared to Llama3 and GPT-4, SKT’s tokenizer shows superior efficiency in Korean and maintains high performance in English as well. Although the performance gap has narrowed with the recent release of GPT-4o, SKT still leads in Korean.
Conclusion
In conclusion, we have explored the importance and role of tokenizers, as well as SKT's optimized tokenizers. Tokenizers are the first step in processing text data and have a significant impact on the performance of LLMs. SKT has developed custom tokenizers tailored to the characteristics of various languages, boasting superior performance compared to competitors.
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