Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation
Dense retrieval ranks one query vector against one document vector. On long documents, this interface can fail when a short but decisive span is weakened during document encoding before ranking. We study this failure mode as document-side early compression and introduce the Evidence Dilution Index (EDI) to measure how far a document-level representation falls below the strongest chunk-level evidence within the same gold document. Guided by this view, we propose DICE (Document Inference via Chunk Evidence), a training-free document-side strategy that splits documents into chunks, encodes them independently with a frozen model, and aggregates them back into a single vector while preserving ...
| Authors | Shanshan Lyu, Yiwei Wang, Yujun Cai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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