A typical approach developers follow to influence an LLM's behavior in an application is through careful manipulation of the prompt, such as by adding or modifying instructions. However, merely adding more instructions provides little assurance that they will actually be followed. We introduce Instruction Boosting as a post-generation method to increase the reliability of LLM prompt instructions. We show that Instruction Boosting improves the instruction following rate by up to 7 points for two instructions and up to 4 points for ten instructions. To demonstrate these results we introduce SCALEDIF, a benchmark with a scaled instruction volume of up to ten instructions per data sample. We also present an analysis of the commonly observed trend that performance degrades as more instructions are added. We show that an important factor contributing to this trend is the degree of tension and conflict that arises as the number of instructions is increased. We contribute a quantitative conflict scoring tool that explains the observed performance trends and provides feedback to developers on the impact that additional prompt instructions have on a model's performance.

翻译：开发人员通常通过精心设计提示词来影响大型语言模型在应用中的行为，例如添加或修改指令。然而，仅增加指令数量并不能确保模型实际遵循这些指令。本文提出指令增强作为一种后生成方法，以提高大型语言模型提示指令的可靠性。实验表明，指令增强可将两条指令的遵循率提升高达7个百分点，十条指令的遵循率提升高达4个百分点。为验证这些结果，我们构建了SCALEDIF基准测试集，其每个数据样本包含最多十条指令的规模化指令集。我们还分析了指令数量增加导致性能下降的常见现象，指出指令间产生的张力与冲突程度是导致该趋势的重要因素。我们开发了一种定量冲突评分工具，该工具能解释观察到的性能变化趋势，并为开发人员提供新增提示指令对模型性能影响的反馈。