Digital Commons Network^™

Mending Trust In Ai: Trust Repair Policy Interventions For Large Language Models In Visual Data Journalism, Hangxiao Zhu

McKelvey School of Engineering Theses & Dissertations

Trust in Large Language Models (LLMs) emerged as a pivotal concern. This is because, despite the transformative potential of LLMs in enhancing the interpretability and interactivity of complex datasets, the opacity of these models and instances of inaccuracies or biases have led to a significant trust deficit among end-users. Moreover, there is a tendency for people to personify AI tools that utilize these LLMs, attributing abilities and sensibilities that they do not truly possess. This thesis exploits this personification and proposes a comprehensive framework of trust repair policies tailored to address the challenges inherent in LLM annotations within data journalism …

Comprehensive Question And Answer Generation With Llama 2, Matous Hybl

MS in Computer Science Theses

Since the introduction of transformers, large language models have proven capable in many natural language processing fields. However, existing systems still face challenges in generating high-quality extractive questions. Base models and public chatbots fall short if the question source or quantity are critical. Our contribution is a question and answer generator for generating comprehensive, extractive questions and answers. This approach includes fine-tuning a LLaMA 2 base model for answer extraction (AE) and question generation (QG). We evaluate the resulting system using common automated metrics and a manual evaluation. We find that our system is comparable to the latest research and …

Sequential Recommendation: From Representation Learning To Reasoning, Lei Wang

Dissertations and Theses Collection (Open Access)

The recommender system is a crucial component of today's online services. It helps users navigate through an overwhelmingly large number of items and discovering those that interest them. Unlike general recommender systems, which recommend items based on the user's overall preferences, sequential recommender systems consider the order of user-item interactions. Sequential recommendations aim to predict the next item a user will interact with, given a sequence of previously interacted items, while considering the short-term and long-term dependencies among items.

In this thesis, we focus on sequential recommendation methods: from representation learning to large language model (LLM)-based reasoning. On the one …