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From Poetry to Programming: The Evolution of Prompt Engineering with Riley Goodside of Scale AI

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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Avoiding the Pitfalls of Fine-Tuning

Fine-tuning a model can lead to significant trade-offs, notably the erosion of general skills and an increased likelihood of hallucination. As models are tuned with new information outside their original knowledge base, they may misconstrue what constitutes valid information, leading to the generation of false data. This occurs because the model, during pre-training, develops a strong understanding of truth based on extensive text. Introducing non-standard information dilutes this instinct, creating a risk of generating inaccuracies. A viable strategy to mitigate these risks includes incorporating more general data alongside specialized information, aiming to uphold the model's foundational knowledge.

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