  Despite the remarkable performance of generative large language models (LLMs)
on abstractive summarization, they face two significant challenges: their
considerable size and tendency to hallucinate. Hallucinations are concerning
because they erode reliability and raise safety issues. Pruning is a technique
that reduces model size by removing redundant weights, enabling more efficient
sparse inference. Pruned models yield downstream task performance comparable to
the original, making them ideal alternatives when operating on a limited
budget. However, the effect that pruning has upon hallucinations in abstractive
summarization with LLMs has yet to be explored. In this paper, we provide an
extensive empirical study across five summarization datasets, two
state-of-the-art pruning methods, and five instruction-tuned LLMs.
Surprisingly, we find that hallucinations from pruned LLMs are less prevalent
than the original models. Our analysis suggests that pruned models tend to
depend more on the source document for summary generation. This leads to a
higher lexical overlap between the generated summary and the source document,
which could be a reason for the reduction in hallucination risk.
