Evaluating Whether LLMs Can Reliably Connect the DOTs?
Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct missing spans within a broader storyline, commonly referred to as text infilling, while preserving consistency with both the local context and the global storyline. Despite using text infilling as a pre-training objective in many Large Language Models (LLMs), their actual performance on real-world narrative infilling remains underexplored. In this paper, we address this gap by introducing a multi-domain benchmark of ~9.2K instances for narrative infilling, constructed by masking one to three sentences across four narrative types: encyclopedic...
| Authors | Eftekhar Hossain, John Salvador, Santu Karmaker |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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