Leaders Opinion: Navigating Overconfidence Challenges in Large Language Models (LLMs)

Developers fine-tuning Language Model (LLM) models often face a challenge known as overconfidence. In an experiment by Jonathan Whitaker and Jeremy Howard of fast.ai, this issue was explored, shedding light on the less-discussed problem of overconfidence in LLMs. Overconfidence occurs when the model asserts incorrect information from the dataset, potentially due to underfitting and overfitting, which represent the balance in the bias-variance tradeoff. Maharaj Mukherjee, Senior Vice President and Senior Architect Lead of Bank of America weighed in on the matter, “One thing that is almost certain for any ML model is the model decay. The model will sooner or later provide erroneous or erratic results with deteriorating value and predictability. Complex systems that depend on multiple mo