1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist is fine-tuning a Llama 2 model on a custom dataset for a summarization task. After fine-tuning, the model produces summaries that are too similar to the input text, often copying sentences verbatim. Which adjustment is MOST likely to reduce copying and improve abstractive summarization?
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 the temperature to 0.8 and use top-p sampling
Increasing temperature (e.g., to 0.8) adds randomness to token selection, making the model less likely to choose the most probable tokens that lead to verbatim copying from the input. Top-p sampling further restricts the sampling pool to a cumulative probability mass, balancing diversity and coherence. In contrast, greedy decoding and beam search always select high-probability tokens, which encourages copying. Reducing context window may lose important information, not fix copying. Thus, higher temperature with top-p sampling is the best adjustment to promote abstractive summarization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the temperature to 0.8 and use top-p sampling
Why this is correct
Higher temperature flattens the probability distribution, making the model more likely to generate novel phrases rather than copying.
- ✗
Switch to greedy decoding
Why it's wrong here
Greedy decoding always picks the highest probability token, which often leads to copying.
- ✗
Increase the beam search width
Why it's wrong here
Beam search with higher width still favors high-probability sequences, potentially increasing copying.
- ✗
Reduce the context window size
Why it's wrong here
Context window size does not directly affect copying; it limits input length.
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