Big Data and Analytics: Evolutionary Analytics
Permanent URI for this collectionhttps://hdl.handle.net/10125/112422
Browse
Recent Submissions
Item type: Item , Using Knowledge Graphs to Test for the Presence of Hallucinations in Closed RAG-based and Custom GPT-based LLM Systems(2026-01-06) Miller, GranvilleMany companies and government agencies are building large language model (LLM) systems for proprietary or specialized uses with sensitive data. These closed systems are not permitted to access the internet as there are concerns about data leakage. At the same time, the absence of internet access limits the data to data contained in the vector database which is used to implement retrieval augmented generation (RAG) or the ingested data in a Custom Generative Pre-trained Transformer (GPT). These systems wish to present authoritative responses and avoid hallucinations, factually inaccurate responses to various prompts. In sensitive government and enterprise environments, hallucinated responses can lead to serious consequences, including misinformation, policy errors, or breaches of trust, making their detection and prevention a critical priority. Using knowledge graphs, we can graph the data or parts of the data and use patterns to predict the areas to test where prompts may create these hallucinations in closed systems. Given the very large number of possible prompts for such a system, being able to target the testing of these systems is extremely important. Once issues are discovered, these graphs can also help developers fix issues where data is missing, erroneous, or must be enhanced using data curation.Item type: Item , Refining Neural Network Interpretability through Activation Modification(2026-01-06) Mammadova, NigarThis research focuses on the problem of how to design real post-hoc modifiable Deep Neural Networks (DNNs) that can achieve or exceed state-of-the-art performance while also providing increased transparency that can help in understanding how predictions made by DNNs were reached. Existing techniques for interpretability are mostly concentrated on inspecting neuron activations as is. Here, we study controlled neuron activation adjustments during inference and examine whether these adjustments can help improve the explainable aspect and generalization of Fully Connected Neural Networks (FCNNs) without retraining. The study introduces three activation method adaptation strategies. All of them introduce a systematic adjustment of neuron activations according to individual activation magnitude, which tends to make the latent feature representation more significant in the inference phase. Experimental results show that the improvement of classification accuracies can be significant on misclassified samples as well as on overall model performance, achieving up to 14% improvements without retraining.Item type: Item , Introduction to the Minitrack on Big Data and Analytics: Evolutionary Analytics(2026-01-06) Kaisler, Stephen; Armour, Frank; Money, William