AWS Certified Cloud Practitioner

Explanation

For legal contract summarization requiring precise, repeatable language, the optimal settings are:

Temperature = 0.2 (low temperature)

Top-p = 0.7 (moderate nucleus sampling)

Why this combination works best:

1. Low Temperature (0.2):

   • Temperature controls the randomness/creativity of the model's output

   • Lower values (closer to 0) make the model more deterministic and focused on the most likely tokens

   • For legal documents, we want minimal creativity and maximum consistency

   • Temperature of 0.2 ensures the model sticks closely to the most probable word choices

2. Moderate Top-p (0.7):

   • Top-p (nucleus sampling) controls the diversity of token selection

   • Lower top-p values restrict the model to a smaller set of high-probability tokens

   • 0.7 allows some flexibility while still maintaining focus on the most relevant vocabulary

   • This prevents the model from considering too many unlikely alternatives

Why other options are incorrect:

• Option B (temperature=0.9, top-p=1.0): Too high temperature introduces randomness, and top-p=1.0 considers all possible tokens, leading to inconsistent results

• Option C (temperature=1.2, top-p=1.0): Even higher temperature makes outputs highly creative and unpredictable, unsuitable for legal precision

• Option D (temperature=0.8, top-p=0.3): While top-p=0.3 is restrictive, temperature=0.8 is still too high for precise legal work

Key takeaway: For tasks requiring precision and repeatability (like legal document processing), use low temperature values combined with moderate top-p settings to balance consistency with appropriate vocabulary selection.