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.
Authors
- Roberto Gioiosa (ORCID: https://orcid.org/0000-0001-9430-2656)
- Zhao Zhang (ORCID: https://orcid.org/0000-0001-5921-0035)
- Lishan Yang (ORCID: https://orcid.org/0000-0002-0735-8617)
- Yu Sun (ORCID: https://orcid.org/0000-0001-7740-6082)
- Zhu Zhu
- Cherish Mulpuru
Institutions
- Rutgers, The State University of New Jersey (US)
- Pacific Northwest National Laboratory (US)
- George Mason University (US)
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