LLMs may systematically degrade narrative quality by rewarding explicit declaration over subtle implication, which matters because these models increasingly evaluate and shape written content.
This paper proposes 'summarization bias'—a tendency of LLMs to flatten narrative meaning into explicit summaries rather than preserving the inferential structure that makes stories work.