Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering

Understand

Training LLMs on data containing unfamiliar knowledge during the instruction tuning stage can encourage hallucinations.

• To address this challenge, we introduce NOVA, a novel framework designed to identify high-quality data that aligns well with the LLM's learned knowledge to reduce hallucinations.
• NOVA includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data.
• Specifically, ICP evaluates the LLM's understanding of the given instruction by calculating the tailored consistency among multiple self-generated responses.