Fine-Tuning Large Language Models for Data Cleaning and Labeling Tasks

20min Snip

00:00

Play full episode

Summary

Transcript

Episode notes

The chapter explores the process of fine-tuning large language models (LLMs) for data cleaning and labeling tasks, explaining the need for a custom model. It discusses tool chains used for transitioning models into production, focusing on periodic training and customer-specific tuning. The impact of LLMs on content generation, challenges in scaling infrastructure, and efforts towards improving model output quality and efficiency are also covered.

Machine learning models learn patterns and relationships from data to make predictions or decisions. The quality of the data influences how well these models can represent and generalize from the data.

Nihit Desai is the Co-founder and CTO at Refuel.ai. The company is using LLMs for tasks such as data labeling, cleaning, and enrichment. He joins the show to talk about the platform, and how to manage data in the current AI era.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .