FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking

Understand

Automatic fact-checking plays a crucial role in combating the spread of misinformation.

• Large Language Models (LLMs) and Instruction-Following variants, such as InstructGPT and Alpaca, have shown remarkable performance in various natural language processing tasks.
• However, their knowledge may not always be up-to-date or sufficient, potentially leading to inaccuracies in fact-checking.
• To address this limitation, we propose combining the power of instruction-following language models with external evidence retrieval to enhance fact-checking performance.