RESEARCH · RESEARCH · #838
MSBD: Multi-Split Boundary Decision for LLM-based page stream segmentation
The paper introduces Multi-Split Boundary Decision (MSBD), a zero-shot approach that predicts multiple document boundaries within a page window in a single LLM call to reduce inference requests. Evaluation across multiple language models, collections, input modalities, and window sizes shows MSBD can substantially improve inference efficiency while preserving segmentation accuracy within a model- and corpus-dependent operating range, but accuracy falls off at larger windows with distinct over- and under-segmentation behaviors.
KEY POINTS
- The paper introduces Multi-Split Boundary Decision (MSBD), a zero-shot approach that predicts multiple document boundaries within a page window in a single LLM call to reduce inference requests.
- Evaluation across multiple language models, collections, input modalities, and window sizes shows MSBD can substantially improve inference efficiency while preserving segmentation accuracy within a model- and corpus-dependent operating range, but accuracy falls off at larger windows with distinct over- and under-segmentation behaviors.
- This matters because MSBD can cut inference cost for zero-shot LLM page-stream segmentation, enabling more efficient large-scale document processing when window size is chosen appropriately.
WHY IT MATTERS
This matters because MSBD can cut inference cost for zero-shot LLM page-stream segmentation, enabling more efficient large-scale document processing when window size is chosen appropriately.