HiCaM combines an LLM-built hierarchical summary tree with a causal entity graph to guide long-document editing, reporting 56.8-72.5% win rates and up to 59.5% net win rates over direct LLM baselines as judged by GPT-4o.
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HiCaM: A Hierarchical-Causal Modification Framework for Long-Form Text Modification
HiCaM combines an LLM-built hierarchical summary tree with a causal entity graph to guide long-document editing, reporting 56.8-72.5% win rates and up to 59.5% net win rates over direct LLM baselines as judged by GPT-4o.