1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An e-commerce company fine-tuned a Cohere Command model on their product catalog to generate product descriptions. During inference, they notice the model outputs are too repetitive: it often repeats similar phrases across different products, and the descriptions lack diversity. The team wants to increase the variety of the generated text without sacrificing relevance. They are currently using temperature=0.8, top_p=0.9, frequency_penalty=0, and presence_penalty=0. Which parameter adjustment should they make to most effectively increase diversity?
⚠ Common exam trap
Oracle often tests the misconception that increasing temperature always increases diversity, when in fact decreasing temperature reduces randomness, and the most effective lever for diversity in a fine-tuned model is often adjusting top-p or adding a positive frequency penalty.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase top_p from 0.9 to 0.95.
Increasing top_p from 0.9 to 0.95 expands the nucleus of tokens considered during sampling, allowing the model to select from a wider set of plausible next tokens. This directly increases output diversity while still maintaining relevance, as tokens outside the top 90% probability mass are now included. The current settings already have moderate temperature and no penalties, so broadening top_p is the most effective single adjustment to reduce repetitiveness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease temperature from 0.8 to 0.5.
Why it's wrong here
Lower temperature makes the model more deterministic, reducing diversity.
- ✗
Set frequency_penalty to a negative value (e.g., -0.5).
Why it's wrong here
Negative penalty encourages repetition, which is the opposite of the desired effect.
- ✗
Increase max_tokens from 200 to 500.
Why it's wrong here
Max_tokens controls output length, not diversity.
- ✓
Increase top_p from 0.9 to 0.95.
Why this is correct
Higher top_p includes more tokens in the sampling pool, increasing diversity.
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1 more way this is tested on 1Z0-1127-25
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Variation 1. Which THREE techniques are commonly used to improve the quality of text generation?
hard- ✓ A.Temperature scaling
- ✓ B.Top-k sampling
- C.Greedy decoding
- D.Random sampling
- ✓ E.Beam search
Why A: Temperature scaling is correct because it controls the randomness of token probability distributions by dividing logits before softmax; lower temperatures (e.g., 0.1) make the model more deterministic, while higher temperatures (e.g., 1.5) increase diversity. This directly influences the quality of generated text by balancing coherence and creativity.
JA
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