  Large language models are limited by challenges in factuality and
hallucinations to be directly employed off-the-shelf for judging the veracity
of news articles, where factual accuracy is paramount. In this work, we propose
DELL that identifies three key stages in misinformation detection where LLMs
could be incorporated as part of the pipeline: 1) LLMs could \emph{generate
news reactions} to represent diverse perspectives and simulate user-news
interaction networks; 2) LLMs could \emph{generate explanations} for proxy
tasks (e.g., sentiment, stance) to enrich the contexts of news articles and
produce experts specializing in various aspects of news understanding; 3) LLMs
could \emph{merge task-specific experts} and provide an overall prediction by
incorporating the predictions and confidence scores of varying experts.
Extensive experiments on seven datasets with three LLMs demonstrate that DELL
outperforms state-of-the-art baselines by up to 16.8\% in macro f1-score.
Further analysis reveals that the generated reactions and explanations are
greatly helpful in misinformation detection, while our proposed LLM-guided
expert merging helps produce better-calibrated predictions.
