Resilience Assessment of Large Language Models under Transient Hardware Faults
Bibliographic record
Abstract
Large Language Models (LLMs) are transforming the field of natural language processing and revolutionizing the way machines interact with humans. LLMs like ChatGPT and Google’s Bard have already made significant strides in conversational AI, enabling machines to understand natural language and respond in a more human-like manner. In addition to typical applications like sentiment analysis and text generation, LLMs are also used in safety-critical applications such as code generation and speech comprehension in autonomous driving vehicles, where reliability is important.In this work, we investigate the resilience of LLMs under transient hardware faults. Specifically, we used IR-level fault injection (FI) to assess the reliability of five popular LLMs, including Bert, GPT2, and T5, under transient hardware faults. Moreover, we also investigate how the resilience of LLMs varies with different pre-training, fine-tuning objectives, and the number of encoder and decoder blocks. We find that LLMs are quite resilient to transient faults overall. We also find that the behavior of the LLM under transient faults varies significantly with the input, LLM’s architecture, and the type of task (e.g., translation vs. fill-in-the-blank). Finally, we find that the Silent Data Corruption (SDC) rate varies with different fine-tuning objectives, and for the fill-mask fine-tuning objective, the SDC rate also increases with the model size. Overall, our findings indicate that the use of LLMs in safety-critical applications needs further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".