提升大模型指令遵循能力，让复杂指令更可靠。
Boosting Instruction Following at Scale
- 提出指令增强方法，后处理生成结果提升遵循率。
- 十条指令下遵循率最高提升4个百分点。
- 提供冲突评分工具，帮助开发者优化提示设计。
开发人员通常通过精心设计提示来控制大语言模型的行为，但增加指令数量并不保证其被遵守。本文提出指令增强（Instruction Boosting）这一生成后处理方法，显著提高指令遵循率：两条指令时最多提升7个百分点，十条指令时最多提升4个百分点。为此，我们构建了SCALEDIF基准，每样本最多包含十条指令。分析表明，随着指令增多性能下降的重要原因是指令间的张力与冲突加剧。我们提出量化冲突评分工具，可解释性能变化趋势，并为开发者提供新增指令对模型表现的影响反馈。
原文摘要 · Abstract (English)
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.
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