Researchers have developed a new method called RCD for selecting relevant subsets of long clinical texts to reduce token costs for large language models. This approach frames the problem as a knapsack-constrained subset selection, balancing relevance, coverage, and diversity. Experiments on various datasets showed that different unitization strategies and selection methods perform best depending on the specific task and budget constraints, with diversity-aware methods like MMR proving beneficial for LLM generation. AI
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IMPACT Optimizes LLM token usage for long clinical documents, potentially lowering operational costs and improving efficiency in healthcare AI applications.
RANK_REASON Academic paper detailing a new method for optimizing LLM input processing.