LLM (Large Language Model) has undoubtedly been one of the most buzzing topics over the past two years, since the release of ChatGPT by OpenAI.
𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗟𝗟𝗠𝘀
Large Language Models are essentially sophisticated AI systems designed to understand and generate human-like text. What makes them large” is the sheer volume of data they’re trained on and the billions of parameters they use to capture the nuances of human language. But remember, while they can generate human-like text, machines don’t “understand” language in the way humans do. Instead, they process data as numbers, thanks to a technique called Natural Language Processing (NLP).
Today, we’ll cover the key NLP techniques used to prepare text data into a machine-readable form for use in LLMs, starting with text pre-processing.
𝗞𝗲𝘆 𝗦𝘁𝗲𝗽𝘀 𝗶𝗻 𝗧𝗲𝘅𝘁 𝗣𝗿𝗲-𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴:
1️⃣ 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻
Tokenization is where it all begins. The model breaks down text into smaller units called tokens, which could be words or even sub-words. For example, the sentence “Working with NLP is tricky” becomes [“Working”, “with”, “NLP”, “is”, “tricky”, “.”]. This step is crucial because it allows the model to understand input text in a structured way that can be processed numerically.
2️⃣ 𝗦𝘁𝗼𝗽 𝘄𝗼𝗿𝗱 𝗿𝗲𝗺𝗼𝘃𝗮𝗹
Not every word in a sentence carries significant meaning. Stop words like “with” and “is” are common across many sentences but add little to the meaning. By removing these, the model can focus on the more meaningful parts of the text, enhancing efficiency and accuracy.
3️⃣ 𝗟𝗲𝗺𝗺𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻
Lemmatization simplifies words to their base form, making it easier for the model to understand the context without getting bogged down by variations. For instance, words like “talking”, “talked”, and “talk” all get reduced to their root form “talk.
We are then ready for the next step, which is to change the text into a form the computer can understand.
[To Be Continued]
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