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Abstract: large language models (LLMs) have exhibited outstanding performance across a wide range of natural language processing (NLP) tasks. However, the rising prevalence of hardware transient faults has made silent data corruptions (SDCs) in LLMs increasingly problematic, severely degrading output quality and user experience. State-of-the-art protection schemes primarily rely on hardware-assisted algorithm-based fault tolerance (ABFT) or boundary-setting-driven online fault tolerance (FT2) for selective layers, yet these solutions suffer from strict hardware dependencies, substantial overhead, or incomplete coverage. To address these limitations, we propose RetryTrigger, a novel hardware-free fault-aware inference methodology capable of handling all potential faults. During LLM inference, RetryTrigger dynamically collects runtime output features (e.g., maximum probability, top-k probability gaps, output entropy, logits statistics, and inference latency), which are used to train a LightGBM meta-model. This meta-model accurately predicts whether duplicate inference should be performed, thereby effectively mitigating faults while preserving efficiency without additional hardware dependence. Extensive experiments on seven representative LLMs (including T5-Small, RoBERTa, BioMedBERT, Qwen2.5-Coder-0.5B/7B, MiniMind, and Opt) demonstrate that RetryTrigger reduces SDC rates by up to 95.33% (on average 92.97%) and achieves a minimal performance overhead of 2.4012% (on average 4.1167%), offering a superior balance between reliability and efficiency compared to state-of-the-art solutions.

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