Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data

Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore techniques for reducing hallucinations in large language models through fine-tuning in this hour-long webinar from Weights & Biases. Learn how to leverage out-of-domain data to improve MistralAI models' ability to detect factual inconsistencies. Follow along with a hands-on demonstration that covers creating initial prompts, setting up evaluation pipelines, and implementing a two-step fine-tuning process using the Factual Inconsistency Benchmark dataset and Wikipedia summaries. Discover how Weights & Biases Weave can automate model evaluation and see examples of innovative fine-tuning applications from hackathon winning projects. Gain insights into Mistral's latest models, fine-tuning services, and conversational AI tools to enhance natural language inference in production environments.