RIFT: Reordered Instruction Following To Evaluate Instruction Following in Singular Multistep Prompt
Автор: AI Papers Podcast Daily
Загружено: 2026-02-02
Просмотров: 5
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The paper introduces RIFT, a novel framework designed to evaluate the instruction-following capabilities of Large Language Models (LLMs) by isolating prompt structure from semantic content. By testing models with rephrased Jeopardy! questions arranged in both sequential linear formats and non-sequential "jumping" configurations, the researchers discovered that model accuracy collapses by as much as 72% when the linear flow is disrupted,. Detailed error analysis reveals that these failures usually stem from the models' inability to adhere to structural commands rather than a lack of factual knowledge, suggesting that current architectures rely on internalized sequential patterns rather than robust reasoning skills to execute tasks,. Ultimately, these findings expose a fundamental limitation in how state-of-the-art models handle non-linear control flow, indicating that their effective capacity for complex, discontinuous instruction following is significantly lower than their nominal context limits suggest,.
https://arxiv.org/pdf/2601.18924
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