Can high-quality data compensate for every model limitation?
No. Architecture, capacity, optimization, and available computation also constrain performance. Better data improves the learning opportunity but cannot guarantee success on every task.
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More FAQs in How does the quality of training data affect an LLM’s performance?
No. A polished passage can be inaccurate, repetitive, irrelevant, or misleading. Quality must be assessed against the training objective and intended application.
They can analyze evaluation failures by topic, language, format, and user need, then inspect whether the dataset contains representative examples of those cases.
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