Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation
Presents a training-free method to improve long-document dense retrieval by aggregating chunk embeddings into a single vector.
š arxiv.org/abs/2606.18781
šØš½āš» github.com/PunchlineAAA...
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 st...
