Boosting Instruction Following at Scale

2025-10-16

AI Summary

Large language models (LLMs) suffer from a pronounced decline in instruction-following accuracy as the number of concurrent instructions increases—a critical limitation for complex, multi-step prompting. To address this, we propose Instruction Boosting: an interpretable, post-hoc reweighting mechanism grounded in generative outputs. Our method introduces a quantified conflict scoring model that, for the first time, identifies semantic tension among instructions as the primary cause of performance degradation and provides actionable, diagnostic feedback on instruction conflicts. To rigorously evaluate multi-instruction adherence, we construct SCALEDIF, a high-scale benchmark comprising instruction combinations ranging from 2 to 10. Experiments demonstrate that Instruction Boosting improves instruction-following accuracy by +7.0 percentage points in dual-instruction settings and +4.0 points in ten-instruction settings, substantially mitigating multi-instruction performance decay. This work advances prompt engineering with both theoretical insight—revealing instruction conflict as a fundamental bottleneck—and a practical, deployable tool for robust multi-instruction execution.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Prompt Engineering / Prompting
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Improving LLM instruction following reliability through post-generation methods
• Addressing performance degradation with increasing instruction volume
• Quantifying conflict between multiple instructions to explain performance trends

Innovation

• Instruction Boosting as post-generation method
• SCALEDIF benchmark with scaled instructions
• Quantitative conflict scoring tool for instructions