איבוד באמצע: ניתוח ופתרון ליכולת יצירת טקסט ארוך

תקציר מקורי באנגליתarXiv:2503.06868v2 Announce Type: replace Abstract: Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored. As input length increases, LLMs increasingly overlook information in the middle of the context--a limitation known as the "lost-in-the-middle" phenomenon--leading to inconsistent and incoherent outputs. To address this problem, we propose Retrieval-Augmented Long-Text Writer (RAL-Writer), a training-free framework consisting of a Planner that generates writing steps and a Writer that produces content according to these steps. The Writer jointly models semantic relevance and positional bias to compute importance scores, dynamically retrieve critical input segments, and strategically restate them. We a