Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped. These errors pose a significant threat to neural network reliability because even small changes in model parameters can lead to large shifts in outputs. Large language models (LLMs) are particularly vulnerable on resource-constrained or outdated hardware. Such hardware often lacks error-correction mechanisms and faces aging issues, leading to instability under the vast parameter counts and heavy computational loads of LLMs. While the impact of BFEs on traditional networks like CNNs is relatively well-studied, their effect on the complex architecture of transformers remains largely unexplored. Firstly, this paper presents a comprehensive systematic analysis of BFE vulnerabilities in key LLM components, revealing distinct sensitivities across parameters, activations, and gradients during fine-tuning and inference. Secondly, based on our findings, we introduce a novel defense strategy FlipGuard: (i) exponent bit protection, and (ii) a self-correction based fine-tuning mechanism, to address BFE consequences. FlipGuard minimizes performance degradation while significantly enhancing robustness against BFEs. Experiments demonstrate a 9.27 reduction in accuracy drop under 1 BFEs on the SST-2 dataset using BERT, and a 36.35-point improvement in perplexity on the Wikitext-103 dataset using GPT-2, compared to unprotected models. These results show the potential of our approach in enabling reliable LLM deployment on diverse and less reliable hardware platforms.

Multimodal Large Language Models (MLLMs) have gained increasing popularity as a promising framework for leveraging the strong language reasoning capabilities in the vision-language domain. Given a wide range of MLLMs, model merging potentially offers a cheap way to aggregate their diverse knowledge into a single MLLM. However, directly plug-in existing model merging approaches often leads to suboptimal performance due to (1) inclusion of harmful models that have over-confident predictions in the target task; (2) the lack of specialized designs for vision-language inputs. To tackle these pain points, we conduct pioneering investigations to dissect the merging procedures and propose an uncertainty-guided MLLM merging algorithm, i.e., UQ-Merge, which i) identifies beneficial candidates for merging, ii) determines the merging order and the number of helpful candidates, and iii) performs appropriate merging. Within our framework, we consider uncertainty quantification on both text and vision inputs to examine the MLLM prediction confidence, and then decide whether and when a MLLM needs to be included. It is worth mentioning that our vision-language uncertainty quantification does not require access to sample labels, making it more practical in various scenarios. Extensive experiments consistently demonstrate the superior MLLM merging performance of UQ-Merge in both held-in and held-out vision-language benchmarks. For example, compared to existing state-of-the-art merging methods, UQ-Merge brings substantial performance improvements of up to 44.3% on average accuracy in 12 datasets. Codes are available at https://anonymous.4open.science/r/UQ-Merge-7CD7.