FT2: First-Token-Inspired Online Fault Tolerance on Critical Layers for Generative Large Language Models

Generative Large Language Models (LLMs) are deployed on large-scale computing systems, where such tasks unavoidably suffer from soft errors, leading to quality degradation of content generated by LLMs. Enhancing LLM resilience is particularly challenging because of its complicated model architecture and tremendous size. State-of-the-art protections have limitations such as high overhead and incomplete coverage, and often require offline profiling.

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Publication Details

Published
2025-07-20
DOI
https://doi.org/10.1145/3731545.3731570
Citations
1
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
2.48
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FT2: First-Token-Inspired Online Fault Tolerance on Critical Layers for Generative Large Language Models

Roberto Gioiosa, Zhao Zhang, Lishan Yang, Yu Sun et al.
1 citations
Adversarial Robustness in Machine Learning
2.48
article

FT2: First-Token-Inspired Online Fault Tolerance on Critical Layers for Generative Large Language Models

Roberto Gioiosa, Zhao Zhang, Lishan Yang, Yu Sun, Zhu Zhu, Cherish Mulpuru
article en
1 citations

Abstract

Generative Large Language Models (LLMs) are deployed on large-scale computing systems, where such tasks unavoidably suffer from soft errors, leading to quality degradation of content generated by LLMs. Enhancing LLM resilience is particularly challenging because of its complicated model architecture and tremendous size. State-of-the-art protections have limitations such as high overhead and incomplete coverage, and often require offline profiling.

Rutgers, The State University of New Jersey (US), Pacific Northwest National Laboratory (US), George Mason University (US)
Openalex Percentile: Top 9%
Adversarial Robustness in Machine Learning
2.48
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