High-quality data is essential for tuning models to solve domain-specific problems. Given that Large Language Models (LLMs) are trained largely on scraped data from the internet, systems that rely on them have a tendency to propagate misinformation or hallucinations due to the inherent bias in the underlying datasets.
Micaela Kaplan, ML Evangelist at HumanSignal, will show you how to develop high-quality domain-specific Q&A datasets for model fine-tuning by leveraging LLMs for dataset curation and integrating human input throughout the process.
In this webinar, we’ll show you how to:
- Generate a large specialized dataset in a cost-effective way using Label Studio
- Fine-tune Llama 3 with this high-quality dataset
- Incorporate human input throughout the process while using LLMs to aid with text generation and automation
This will be a highly-technical and actionable demo, so you won’t want to miss it. We’ll see you there!
Speakers
Machine Learning Evangelist
Micaela Kaplan is the Machine Learning Evangelist at HumanSignal. With her background in applied Data Science and a masters in Computational Linguistics, she loves helping other understand AI tools and practices.