Teach your LLM to answer with facts, not fiction

The article's focus is on improving the factual accuracy of Large Language Models (LLMs). This is a crucial area of research as LLMs are prone to generating incorrect or fabricated information. The title suggests a practical approach to address this problem.

• •Addresses the problem of LLM hallucination (generating false information).
• •Suggests a method to improve the reliability of LLM outputs.
• •Implies a focus on training or fine-tuning LLMs to be more factually accurate.