A municipal bus-stop snow-clear pack used last year’s cases that almost never included curb-cut routes; after fine-tuning, new packs still skip those streets. Which fine-tuning challenge does this show?
Select an answer to reveal the explanation.
Short Explanation
If the teaching set rarely shows curb-cuts, the tuned model will keep skipping them. High-quality, task-specific data matters; thin or biased sets produce thin or biased results. That is a fine-tuning data challenge, not the Chapter 3 output-bias catalog by itself.
Full Explanation
Fine-tuning depends on high-quality, task-specific training data. Biased or incomplete sets — such as snow-clear cases that omit curb-cut routes — produce biased or inaccurate tuned outputs. Bias as an LLM output defect class is covered in Chapter 3; here the focus is the tuning-data challenge.