研究人员开发了一种名为RCD的新方法，用于选择长临床文本的相关子集，以降低大型语言模型的令牌成本。该方法将问题构建为背包约束的子集选择，平衡相关性、覆盖率和多样性。在各种数据集上的实验表明，不同的单元化策略和选择方法在特定任务和预算限制下表现最佳，而像MMR这样的多样性感知方法对LLM生成有益。 AI
影响 优化长临床文档的LLM令牌使用，可能降低医疗保健AI应用的运营成本并提高效率。
排序理由 学术论文，详细介绍了优化LLM输入处理的新方法。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.00336v1 Announce Type: new 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…
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 …