Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries
Quick summary
arXiv:2604.06416v2 Announce Type: replace-cross Abstract: Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace. We evaluate one such understanding task: generating summaries of novels. When human authors of summaries compress a story, they reveal what they consider narratively important. Therefore, by comparing human and LLM-authored summaries, we can assess whether models mirror human patterns of conceptual engagement with texts. To measure conceptual engagement, we align sentences from 150 human-writte
Key takeaways
- arXiv:2604.06416v2 Announce Type: replace-cross Abstract: Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace.
- We evaluate one such understanding task: generating summaries of novels.
- When human authors of summaries compress a story, they reveal what they consider narratively important.
Why it matters
“Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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