研究大模型推理中错误传播规律，发现不同错误影响差异大。
Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference
- 构建可配置的故障注入框架，系统测试三类大模型
- 揭示错误在推理中传播的17条关键规律，识别脆弱模式
- 提出四种无需硬件改动的软件可靠性提升方案
大型语言模型（LLMs）正被广泛应用于高性能计算（HPC）工作流中，通过代码生成和领域特定决策等多元视角加速科学发现。然而，软错误如何在LLM推理中传播并产生影响仍缺乏系统研究。为填补这一空白，我们提出一个名为LLMFI的可配置、确定性的故障注入框架，对三款开源权重的LLM在十三个代表性任务上进行系统性故障注入，覆盖推理、多语言、数学与编程等场景。通过细粒度案例研究，揭示了关键的脆弱性模式。研究共总结出17项核心结论，深化了对错误传播机制的理解，并提出四种低开销的纯软件改进方向，为未来错误检测与缓解提供实用指导。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as code generation and domain-specific decision-making. Yet, how soft errors propagate and affect LLM inference remains largely unexplored. To bridge this gap, we present a comprehensive study on error propagation in LLM inference, enabled by our proposed LLMFI, a configurable and deterministic fault-injection framework. Using LLMFI, we systematically inject faults across three open-weighted LLMs and thirteen representative tasks, covering reasoning, multilingual, mathematical, and coding domains. In addition, we conduct fine-grained case studies that reveal critical vulnerability patterns. Overall, our study yields 17 takeaways that advance the understanding of error propagation in LLM inference and introduces four low-overhead directions to improve reliability through software-only modification, offering practical guidance for future error detection and mitigation.
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