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Demystifying Text Generation in Language Models: Understanding Tokens and Sequential Predictions

IT, Programming, & Web Development › Forums › AI & Machine Learning › Demystifying Text Generation in Language Models: Understanding Tokens and Sequential Predictions

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  • December 15, 2024 at 10:11 am #3825
    Splendid Digital Solutions
    Keymaster

      Disclaimer: This article was created with the assistance of an AI language model and is intended for informational purposes only. Please verify any technical details before implementation.

       

      Understanding how language models (LLMs) generate text one token at a time requires a foundational understanding of tokenization and the model’s function. Let’s break it down:

      What is a Token?

      A token is a unit of text that the model processes and generates. Tokens are not necessarily words—they can represent:
      1. Words (e.g., “cat”).
      2. Subwords (e.g., “play” in “playing”).
      3. Punctuation (e.g., “.”).
      4. Characters (e.g., “a” or “b”).
      5. Special symbols (e.g., or).

      Example:

      For the sentence:

      "I love cats."
      • Tokenization might split this into:
      ["I", "love", "cats", "."]

      These tokens are then converted into IDs (numbers) that the model understands, such as:

      [1, 345, 789, 56]

      Why Tokens?

      Models process text as numerical input, not plain text. Tokenization converts text into a sequence of numbers, which can then be fed into the model.

      What is a Model?

      A model is the core component of an LLM. It takes tokens as input, processes them, and generates probabilities for the next token.

      Key Components of a Model:

      1. Transformer Architecture:
        Most modern LLMs, like GPT and BERT, use the transformer architecture, which processes tokens efficiently and captures relationships between them.

      2. Input Processing:

      – The model takes a sequence of token IDs (e.g., [1, 345, 789]).
      – It applies layers of transformations to predict the likelihood of each possible token being the next one in the sequence.

      1. Output:

      – The model outputs a probability distribution over the entire vocabulary (all possible tokens).
      – For example:

      Input: "I love"
Output Probabilities:
"cats": 70%
"dogs": 20%
"ice cream": 10%

      The token with the highest probability is usually selected as the next token.

      Tokenizer and Model in HuggingFace

      Tokenizer:

      • Functionality:
        The tokenizer splits input text into tokens and converts them into numerical IDs. It also performs the reverse operation: converting token IDs back into human-readable text.

    • Process:

      1. Text to Tokens: "I love cats." → ["I", "love", "cats", "."]
      2. Tokens to IDs: ["I", "love", "cats", "."] → [1, 345, 789, 56]
      3. IDs to Tokens/Text (Decoding): [1, 345, 789, 56] → "I love cats."
      • Example Tokenizer from HuggingFace:
      from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokens = tokenizer.tokenize("I love cats.")
print(tokens) # ['I', 'Ġlove', 'Ġcats', '.']
ids = tokenizer.convert_tokens_to_ids(tokens)
print(ids) # [40, 1845, 837, 13]

      Model:

      • Functionality:
        The model predicts the next token based on the input tokens. It uses self-attention mechanisms in its layers to understand the relationships between tokens.

      • Example Model from HuggingFace:

      from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gpt2")
      • Workflow:
      1. The input IDs [1, 345] are fed into the model.
      2. The model predicts the probabilities of all possible next tokens.
      3. The most probable token (e.g., "cats") is selected.
      4. The process repeats with the new input: [1, 345, 789].

      Generating Text One Token at a Time

      Here’s how it works in practice:

      1. Start with a prompt:
        Example: "I love"

    • Tokenize the prompt:

      • – "I love" → [1, 345]

      1. Feed tokens to the model:

      – Input: [1, 345]
      – Output: Probabilities for the next token.

      1. Generate the next token:

      – The model predicts "cats" (token ID 789).

      1. Add the new token to the sequence:

      – New input: [1, 345, 789]

      1. Repeat until stopping criteria:

      – Stop when reaching a specified token length, the “ token, or when the user decides.

      Why Generate One Token at a Time?

      1. Sequential Dependence:

      – Each token depends on the tokens that precede it. Generating one token at a time ensures that the context evolves naturally.

      1. Flexibility:

      – Generating token-by-token allows dynamic adjustments, like sampling different tokens for creativity or ensuring coherence.

      1. Real-Time Applications:

      – Systems like chatbots or auto-complete tools rely on step-by-step token generation for responsiveness.

      Illustrative Example

      Prompt: "The sun is"

      1. Tokenize:
        "The sun is" → [58, 620, 330]

    • Feed into the model:
      Input: [58, 620, 330]
      Output: Probabilities for the next token:

      • "shining" (70%), "bright" (25%), "hot" (5%)
      1. Select the next token:

      – "shining" (ID 921).

      1. Update the sequence:

      – [58, 620, 330, 921] → "The sun is shining"

      1. Repeat:

      – Continue until reaching an “ token or a desired length.

      By breaking down text generation into these steps, we can see how LLMs create coherent and contextually relevant outputs token-by-token, simulating a natural flow of language.

      Udacity Generative AI nanodegree

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