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Some models struggle to confidently generate responses, leading to hesitation, incomplete answers, or repeated disclaimers. For example, consider this prompt and response:
Model Response: “Well, there are many aspects to climate change. Some people think it’s caused by humans, and others think it’s just natural. It’s hard to say exactly.”

What went wrong?

  • The prompt did not provide enough context for confident decision-making
  • The model allowed too much randomness in token selection
  • The prompt was ambiguous in the response it expected

How it showed up in metrics

  • Mid-range Instruction Adherence: The model understood the instructions but lacked decisiveness

Improvements and solutions

For the following improvements, we will be showing how we could change a simple prompt script like the below example:
1

Provide Stronger Context in Prompts

Include explicit guiding statements, for example:
This should reduce the uncertainty and perplexity in your metrics on Galileo.
2

Adjust Model Sampling Parameters

Lower temperature to make the model more deterministic, for example:
Use top-k sampling to limit options and prevent hesitation, for example:
Lowering the temperature and decreasing top_k both generally increase the prompt adherence.
3

Modify Prompt Structure

Use direct phrasing to force a single, clear response, for example:
Avoid prompts that allow multiple equally valid answers, for example avoiding confusing by adding the source of the opinion we care about: