Budget-Aware Routing for Long Clinical Text
A key challenge for large language models is token cost per query and overall deployment cost. Clinical inputs are long, heterogeneous, and often redundant, while downstream tasks are short and high stakes. We study budgeted context selection, where a subset of document units is chosen under a strict token budget so an off-the-shelf generator can meet fixed cost and latency constraints. We cast this as a knapsack-constrained subset selection problem with two design choices, unitization that defines document segmentation and selection that determines which units are kept. We propose \textbf{RCD}, a monotone submodular objective that balances relevance, coverage, and diversity. We compare s...
| Authors | Khizar Qureshi, Geoffrey Martin, Yifan Peng |
|---|---|
| Primary category | cs.CL |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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