Vocabulary diversity in Large Language Models (LLMs) is governed by a delicate interplay between fine-tuning, which shapes the model's underlying probability distribution, and Top P (nucleus sampling), which filters that distribution during generation. Fine-tuning typically reduces vocabulary diversity by sharpening the model's focus on specific domains or response styles like medical data or concise JSON outputs, effectively narrowing the "nucleus" of probable tokens. To counteract this "distribution collapse" and restore diversity in a fine-tuned model, Top P generally needs to be adjusted upward (ranging from 0.90 to 0.95 or 0.99). Conversely, prompts act as the steering mechanism; an open-ended prompt increases the potential for diverse vocabulary, while a highly constrained prompt ("Answer with yes/no") renders Top P settings mostly irrelevant. Therefore, to maximize diversity in a specialized model, one should combine a higher Top P setting with open-ended prompting to force the model to explore the "tail" of its now-sharpened probability curve.
Influence Matrix on Vocabulary Diversity
| Factor | Primary Function | Effect on Vocabulary Diversity | Recommended Adjustment for Fine-Tuned Models |
|---|---|---|---|
| Fine-Tuning | Specializes the model's weights to a specific domain or style. | Decreases Fine-tuning often "sharpens" the probability distribution, making the model more confident in a smaller set of words (overfitting to the training style). |
Monitor Closely If the model becomes too repetitive, use techniques like Possibility Exploration Fine-Tuning or simply retrain with more diverse data. |
| Top P (Nucleus Sampling) | Filters the token selection pool during generation (decoding). | Controls High P (0.9+): Increases diversity by considering a wider range of tokens. Low P (<0.5): Decreases diversity by cutting off less probable synonyms. |
Increase (↑) Since fine-tuning sharpens confidence, a standard Top P 0.9 might now capture fewer words. Push Top P higher 0.95+ to force the model to consider synonyms it has learned to "ignore." |
| Prompt Engineering | Sets the context, constraints, and tone. | Steers Open-Ended: Allows Top P to have maximum effect. Constrained: Overrides diversity settings like "List 3 items" regardless of Top P. |
Encourage Variance Use instructions like "Use varied vocabulary" or "Avoid repetition" to explicitly fight the fine-tuned model's tendency to converge on rote phrases. |
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