Your company needs to generate product summaries for vendors. You evaluate a foundation model from Model Garden for text summarization and find the style of the summaries are not aligned with your…

Question

Options

• AReplace the pre-trained model with another model in Model Garden.
• BFine-tune the model using a company-specific dataset.
• CIncrease the model's temperature parameter.
• DTune the token output limit in the response.

• A
  3% (1)
• B
  70% (23)
• C
  21% (7)
• D
  6% (2)

Explanation

When a model's output style doesn't align with your brand voice, the root cause is that the pre-trained model was trained on general data, not your company's specific tone and style. Fine-tuning on a company-specific dataset (B) teaches the model to mimic your brand voice by updating the model weights on representative examples. Switching models (A) changes capabilities but won't fix brand alignment. Increasing temperature (C) increases randomness/creativity, not style consistency-it would likely make alignment worse. Adjusting token output limit (D) only controls response length, not content quality or style.

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