Budget-Aware Routing for Long Clinical Text
📰 ArXiv cs.AI
Learn to optimize budget-aware routing for long clinical text using subset selection and knapsack constraints to reduce token costs and latency
Action Steps
- Cast the budgeted context selection problem as a knapsack-constrained subset selection problem
- Formulate the objective function to minimize token costs and latency
- Apply dynamic programming to solve the subset selection problem under the knapsack constraint
- Evaluate the performance of the budget-aware routing approach using clinical text datasets
- Compare the results with baseline models to demonstrate the effectiveness of the proposed approach
Who Needs to Know This
NLP engineers and researchers working on large language models for clinical text analysis can benefit from this approach to optimize their models' performance and reduce costs
Key Insight
💡 Budget-aware routing can significantly reduce token costs and latency for large language models in clinical text analysis
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Optimize budget-aware routing for long clinical text using knapsack constraints and subset selection #NLP #clinicaltext
Key Takeaways
Learn to optimize budget-aware routing for long clinical text using subset selection and knapsack constraints to reduce token costs and latency
Full Article
Title: Budget-Aware Routing for Long Clinical Text
Abstract:
arXiv:2605.00336v1 Announce Type: cross Abstract: 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 prob
Abstract:
arXiv:2605.00336v1 Announce Type: cross Abstract: 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 prob
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