Unverified50% confidenceOpinionExact time
Large models' ability to follow instructions decreases as instruction complexity increases.
1
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50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
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Related Claims
Unverified研究表明LLM在处理过长、过复杂的指令集时整体遵循率会系统性下降，即指令遗忘现象77% similarUnverifiedPrompt engineering has inherent limitations: the effectiveness of instructions decays over conversation turns (instruction forgetting), and different model versions respond significantly differently to the same instruction.76% similarUnverifiedModel compression techniques (distillation, pruning, quantization) incur minimal loss on high-frequency, regular tasks, but significantly degrade multi-step reasoning that requires long-range dependencies—a capability that cross-component state management in front-end interaction code heavily relies on.76% similarVerifiedOverly verbose, contradictory, or ambiguous system prompts cause the model's attention to become scattered, resulting in over-thinking or instruction-following failures; streamlining agents.md can improve the determinism of instruction following.75% similarUnverified随着指令数量增加，LLM的遵循质量会整体下降，开始无差别忽略所有指令而非仅忽略靠后的指令75% similar
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