Prompt frequency tuning refers to the calibration of the Frequency Penalty hyperparameter during the inference stage of a Large Language Model (LLM). This process works by applying a cumulative penalty to a token's selection probability (logit) every time it appears in the generated sequence. By adjusting this setting, users can effectively "fine-tune" the model's output behavior to control vocabulary repetition: increasing the penalty lowers the probability of a word being selected again, forcing the model to access a broader vocabulary and use synonyms. This is distinct from the Presence Penalty, which applies a flat penalty regardless of count; the Frequency Penalty scales dynamically, making it the primary lever for preventing verbatim loops and excessive redundancy while maintaining coherent topic focus.
| Adjustment Strategy | Parameter Setting | Mechanism of Action | Effect on Vocabulary | Ideal Use Case |
|---|---|---|---|---|
| Eliminate Verbatim Loops | Increase Frequency Penalty ranging +0.5 to +1.0 | Penalizes tokens proportionally to how often they have already been used. | Drastically reduces exact word repetition; forces the model to choose synonyms. | Creative writing, preventing "looping" errors, paraphrasing text. |
| Encourage Thematic Shifts | Increase Presence Penalty ranging +0.5 to +2.0 | Applies a one-time penalty if a token exists in the text at all. | Discourages staying on the same topic or using related keywords repeatedly. | Brainstorming diverse ideas, moving a story forward, changing subjects. |
| Allow Natural Repetition | Decrease/Zero Frequency Penalty (0.0) | Removes the cost for reusing words. | Allows words to be repeated as grammatically or factually necessary. | Technical documentation, coding (where variable names must repeat), legal text. |
| Broaden Word Choice | Increase Temperature ranging 0.7 to 1.0+ | Flattens the probability distribution of potential next words. | Increases the chance of selecting lower-probability (rare) synonyms. | Poetry, creative brainstorming, generating "unpredictable" vocabulary. |
| Focus Vocabulary | Decrease Temperature ranging 0.0 to 0.3 | Sharpens the probability distribution towards the most likely tokens. | Results in highly deterministic, repetitive, and "safe" vocabulary usage. | Factual Q&A, data extraction, logic puzzles. |
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