Large Language Models (LLMs) are powerful tools used in several applications such as conversational AI, and code generation. However, significant robustness concerns arise with LLMs in production, such as hallucinations, prompt injection attacks, harmful content generation, and challenges in maintaining accurate domain-specific content moderation. Guardrails aim to mitigate these challenges by aligning LLM outputs with desired behaviors without modifying the underlying models. Nvidia NeMo Guardrails, for instance, rely on specifying acceptable/unacceptable behaviours. However, it is challenging to predict and address potential issues of LLMs in advance to create these guardrails. Also, manual updates from software engineers are often required to maintain and refine these guardrails. We introduce LLM-Guards, specialised machine learning (ML) models trained to function as protective guards. Additionally, we present an automation pipeline for training and continual fine-tuning of these guards using reinforcement learning from human feedback (RLHF). We evaluated several small LLMs, including Llama-3, Mistral, and Gemma, as LLM-Guards for challenges such as moderation and detecting off-topic queries, and compared their performance against NeMo Guardrails. The proposed Llama-3 LLM-Guard outperformed NeMo Guardrails in detecting offtopic queries, achieving an accuracy of 98.7% compared to 81%. Furthermore, the LLM-Guard detected 97.86% of harmful queries” surpassing NeMo Guardrails by 19.86%.

The rapid growth of both the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) results in a high demand for AI applications in devices. To achieve high levels of accuracy, AI applications typically require a large amount of annotated data. Accessing such data is challenging in various applications such as healthcare, finance and information security. Federated learning (FL) is one of the strategies that was proposed to overcome this challenge. Specifically, FL enables the AI model in the centralized system to be trained without any prior knowledge of the information on the devices. Recent FLs have the disadvantage that they are dependent upon a centralized system, and thus are susceptible to single points of failure. This paper proposes a strategy that employs FL in a decentralized environment where devices can communicate with each other to increase the accuracy of the AI model in each device. Furthermore, we evaluate the proposed strategy in the image classification task with the ResNet50 architecture and the CIFAR-10 dataset. The evaluation shows that the ResNet50 model trained in the decentralized environment can achieve comparable results to the model trained in the centralized environment.

Machine reading comprehension (MRC) is a challenging task in natural language processing that demonstrates the language understanding of the machine. An approach to tackle this challenge requires the machine to answer the question about the given context when needed and abstain from answering when there is no answer. Recent works attempted to solve this challenge with various comprehensive neural network architectures for sequences such as SAN, U-Net, EQuANt, and others that were trained on the SQuAD 2.0 dataset containing unanswerable questions. However, the robustness of these approaches has not been evaluated. In this paper, we propose a data augmentation approach that converts answerable questions to unanswerable questions in the SQuAD 2.0 dataset by altering the entities in the question to its antonym from ConceptNet which is a semantic network. The augmented data is, then, fitted into the U-Net question answering model to evaluate the robustness of the model.