LLM - HypyerSetting

• LLM - HypyerSetting
  • HypyerSetting
  • Top_k
  • Temperature
  • Top_p (Nucleus Sampling)
  • Length_penalty
  • Frequency_penalty:
  • Random_seed:
  • Combined Effects:

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LLM - HypyerSetting

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HypyerSetting

Both top_p and temperature are parameters used to control the randomness and creativity of text generated by a language model, but they do so in different ways:

• Temperature
  • affects how confident or random the model is overall

    .

  • Lower values make the model more focused and repetitive;
  • higher values make it more exploratory and varied.
  • affects the predictability of each word:
  • Lower temperatures lead to more predictable and repetitive outputs,
  • higher temperatures increase the variety and creativity.
• Top_p
  • limits the pool of words the model can choose from based on their combined probabilities

    , focusing on the most likely options until their cumulative probability meets the threshold.

  • affects the range of words the model considers:
  • Higher top_p values allow for a broader selection of words,
  • lower values restrict the model to the most likely words, reducing diversity.
• Top_k
  • limits the number of words the model considers at each step, whereas top_p sets a probability threshold for selecting words.

Easy Analogy:

• Temperature: Think of it as adjusting a spice level in food. Low temperature is like making a dish very mild (predictable and consistent), while high temperature is like making it very spicy (random and varied flavors).

• Top_p: Think of it as setting a cut-off for a buffet. Top_p = 0.9 is like saying you can choose from the top 90% of the most popular dishes, ignoring the less popular ones.

• Top_k: Imagine you are at a voting booth with a list of candidates. If you have a list of the top 3 candidates (Low Top_k), you are limited to choosing from these few. If you have a list of the top 50 candidates (High Top_k), you have a lot more options to choose from, making the decision less predictable.

• Length_penalty: Imagine you’re writing a story. A high length penalty makes you end your story quickly, while a negative length penalty encourages you to add more details and lengthen the story.

• Frequency_penalty: Think of it as a reminder to avoid repeating the same words. With a high frequency penalty, you’re encouraged to use synonyms and vary your language.

• Random_seed: Consider it a recipe book. Using the same recipe (seed) will always give you the same dish (output). Changing the recipe (seed) gives you a different dish, and not using a recipe gives you a new, unique dish each time.

Using these parameters, you can control the balance between creativity and predictability in the model’s outputs.

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Top_k

• What it does: Top_k controls the randomness of the model's output by limiting the number of possible next words the model can choose from to the top k most probable words.

• How it works: When generating text, the model looks at all possible next words and ranks them by their probabilities. It then only considers the top k words for selection and discards the rest.
  • a smaller k value means the model is more focused and less diverse,
  • a larger k allows for more variability.
• Examples:
  • language model using the example prompt: "Once upon a time, in a distant land, there lived a wise old owl who loved to..."
    • Low Top_k (3): The model can choose from the top 3 most probable next words, leading to more focused and less diverse outputs. "Once upon a time, in a distant land, there lived a wise old owl who loved to teach young animals about the forest."
    • High Top_k (50): The model can choose from the top 50 most probable next words, allowing for more diverse and creative outputs. "Once upon a time, in a distant land, there lived a wise old owl who loved to explore ancient ruins, discover hidden treasures, and solve the mysteries of the night sky."

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Temperature

• What it does: Temperature controls the randomness of the model's output by scaling the probabilities of the next word choices .

• How it works:
  • When the temperature is set to a low value (e.g., 0.1), the model becomes more confident and deterministic, often choosing the most probable words, which leads to more conservative and repetitive outputs.
  • When the temperature is high (e.g., 1.0 or higher), the model’s output becomes more random and diverse, as it gives more consideration to less probable words.
• Example:
  • Imagine you are trying to pick a word to complete the sentence "The cat is on the...":
    • Low temperature (0.1): The model might always pick "roof" if it’s the highest probability word.
    • High temperature (1.0): The model might pick "roof," "bed," "table," or even "mountain," providing more variety.
  • language model using the example prompt: "Once upon a time, in a distant land, there lived a wise old owl who loved to..."
    • Low Temperature (0.2): The output will be more deterministic and less diverse. "Once upon a time, in a distant land, there lived a wise old owl who loved to teach young animals about the forest and help them learn new things."
    • High Temperature (1.0): The output will be more creative and diverse. "Once upon a time, in a distant land, there lived a wise old owl who loved to explore hidden caves, decipher ancient scrolls, and unravel the mysteries of the stars."

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Top_p (Nucleus Sampling)

• What it does: Top_p sets a threshold to choose from a subset of the most probable next words whose cumulative probability reaches a specified value (p).

• How it works: The model sorts all possible next words by their probabilities and then selects from the smallest subset of these words that together have a cumulative probability of p.
  • For example, if top_p is set to 0.9, the model will consider only the top 90% probable words.

• Example:

  • Imagine you are trying to pick a word to complete the sentence "The cat is on the...":
    • Top_p = 0.9: The model might choose between “roof,” “bed,” and “table” if these words together account for 90% of the probability distribution, ignoring less likely words like “mountain.”
  • language model using the example prompt: "Once upon a time, in a distant land, there lived a wise old owl who loved to..."
    • High Top_p (0.9): The model considers a larger pool of words, leading to more variety. "Once upon a time, in a distant land, there lived a wise old owl who loved to watch the moonlight dance on the river, listen to the whispers of the wind, and share stories with the night creatures."
    • Low Top_p (0.5): The model considers a smaller, more probable set of words, leading to less diversity. "Once upon a time, in a distant land, there lived a wise old owl who loved to teach young animals and tell them stories about the forest."

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Length_penalty

• What it does: Length_penalty modifies the probability of generating longer or shorter sequences.

• How it works: When generating text, the model typically prefers certain lengths for sequences. A length penalty adjusts the log probabilities of sequences based on their lengths.
  • Positive values penalize longer sequences, encouraging shorter responses,
  • negative values do the opposite.

• Example:

  • language model using the example prompt: "Once upon a time, in a distant land, there lived a wise old owl who loved to..."
    • Length_penalty = 1.0 (neutral): The model generates text without any bias toward length. "Once upon a time, in a distant land, there lived a wise old owl who loved to share stories."
    • Length_penalty = 2.0 (positive, penalizes length): The model prefers shorter outputs. "Once upon a time, there lived an owl."
    • Length_penalty = -1.0 (negative, rewards length): The model generates longer, more elaborate outputs. "Once upon a time, in a distant land surrounded by tall, ancient trees and flowing rivers, there lived a wise old owl who loved to share stories, explore the depths of the forest, and gather the animals for nightly tales."

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Frequency_penalty:

• What it does: Frequency_penalty discourages the model from repeating the same words or phrases.

• How it works: When a word is used multiple times, the model reduces the probability of that word being used again. This helps generate more varied and interesting text.

• Example:

  • language model using the example prompt: "Once upon a time, in a distant land, there lived a wise old owl who loved to..."
    • Frequency_penalty = 0.0 (no penalty): The model may repeat words more often. "Once upon a time, in a distant land, there lived a wise old owl who loved to tell stories. The owl's stories were loved by all the animals."
    • Frequency_penalty = 1.0 (high penalty): The model avoids repeating words. "Once upon a time, in a distant land, there lived a wise old owl who enjoyed narrating tales. The wise bird's narratives were cherished by all creatures."

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Random_seed:

• What it does: Random_seed sets the starting point for the random number generator used in text generation.

• How it works: By setting a specific seed value, you can ensure that the text generation process is repeatable.
  • Using the same seed with the same input will always produce the same output.
  • Different seeds produce different outputs.

• Example:

  • language model using the example prompt: "Once upon a time, in a distant land, there lived a wise old owl who loved to..."
    • Random_seed = 42: Generates a specific, repeatable output. "Once upon a time, in a distant land, there lived a wise old owl who loved to share wisdom with the forest creatures."
    • Random_seed = 123: Generates a different specific, repeatable output. "Once upon a time, in a distant land, there lived a wise old owl who enjoyed teaching the young animals about the mysteries of the forest."
    • Random_seed not set (random each time): Generates different outputs each time.
      • Run 1: "Once upon a time, in a distant land, there lived a wise old owl who spent its days exploring ancient ruins."
      • Run 2: "Once upon a time, in a distant land, there lived a wise old owl who delighted in singing songs to the stars."

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Combined Effects:

• Low Temperature (0.2) and High Top_p (0.9):
  • “Once upon a time, in a distant land, there lived a wise old owl who loved to teach young animals about the forest, share wisdom, and help them grow."
• High Temperature (1.0) and High Top_p (0.9):
  • “Once upon a time, in a distant land, there lived a wise old owl who loved to fly over misty valleys, converse with mystical spirits, and discover new adventures with her forest friends."
• Low Temperature (0.2) and Low Top_p (0.5):
  • “Once upon a time, in a distant land, there lived a wise old owl who loved to teach young animals and help them understand the forest."
• High Temperature (1.0) and Low Top_p (0.5):
  • “Once upon a time, in a distant land, there lived a wise old owl who loved to teach, explore, and guide the forest creatures through their daily lives."