In this article, you will learn how to fine-tune an agentic AI system holistically, covering all four critical dials: training data, parameter-efficient fine-tuning, runtime hyperparameters,...
*Equal Contributors Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for -- the diagnosis of which requires expert knowledge that is not always…
Fine-tuning remains a cornerstone technique for adapting general-purpose pre-trained large language models (LLMs) models (also called foundation models) to serve more specialized, high-value downstream tasks, even as zero- and few-shot methods gain traction.