FactLLaMA: Optimizing Instruction-Following Language Models with
  External Knowledge for Automated Fact-Checking
Tsun-Hin Cheung Kin-Man Lam
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. Our approach involves leveraging search
engines to retrieve relevant evidence for a given input claim. This external
evidence serves as valuable supplementary information to augment the knowledge
of the pretrained language model. Then, we instruct-tune an open-sourced
language model, called LLaMA, using this evidence, enabling it to predict the
veracity of the input claim more accurately. To evaluate our method, we
conducted experiments on two widely used fact-checking datasets: RAWFC and
LIAR. The results demonstrate that our approach achieves state-of-the-art
performance in fact-checking tasks. By integrating external evidence, we bridge
the gap between the model's knowledge and the most up-to-date and sufficient
context available, leading to improved fact-checking outcomes. Our findings
have implications for combating misinformation and promoting the dissemination
of accurate information on online platforms. Our released materials are
accessible at: https://thcheung.github.io/factllama.