Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference
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 cond...
| Authors | Yafan Huang, Sheng Di, Guanpeng Li |
|---|---|
| Primary category | cs.DC |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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