Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries

The paper investigates how large language models (LLMs) engage with long-form narratives by comparing their generated novel summaries to human-authored ones. Researchers align sentences from 150 human-written summaries to specific chapters, highlighting the challenge of this alignment task and the complexity of summarization. They find stylistic differences and that LLMs tend to focus more on the ends of texts, suggesting insights into why models may struggle with narrative comprehension.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• nlp
• benchmarks
• safety