This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
Search
Skip to Search Results- 6Large Language Models
- 2Natural Language Processing
- 1Automated Essay Scoring
- 1Automatic Feedback Generation
- 1Computer Vision
- 1Cross-Lingual
-
Fall 2024
We evaluate the effectiveness of Large Language Models (LLMs) in assessing essay quality, focusing on their alignment with human grading processes. Specifically, we investigate the applicability of LLMs such as GPT-3.5T and Llama-2 in the Automated Essay Scoring (AES) task, a crucial natural...
-
Can AI Offer Effective Feedback for Open-Ended Questions? Results from Fine-Tuned LLMs for Automatic Feedback Generation
DownloadFall 2024
The value of timely, personalized, detailed, and actionable feedback for learning is widely recognized; yet its provision is time-consuming and unfeasible in large-scale contexts. Current automatic feedback generation (AFG) methods lack customization and educator involvement. Large language...
-
Constructing Knowledge Graphs with Language Models and Learning Hierarchies from Graphs using Probabilistic Topic Modeling
DownloadFall 2024
Knowledge graphs leverage a data model structured as a graph or topology to represent and manipulate data. Knowledge graphs, abbreviated as KGs, consist of interconnected factual statements, conceptualized as distinct entities referred to as the {\em subject} and {\em object}, linked by a...
-
Fall 2024
Large Language Models (LLMs), including Vision Large Language Models (VLLMs), herald the coming of a new research epoch in machine learning and computational linguistics. Despite most LLMs being predominantly trained on English, their proficiency in various languages has been confirmed by many...
-
Fall 2024
This thesis addresses the task of few-shot style-conditioned text generation using large language models (LLMs). We propose a novel, model-agnostic approach for adapting LLMs to arbitrary styles using a few text samples from a certain author. Instead of using pre-defined features, our method...
-
Fall 2023
This thesis introduces a new approach for grounding concepts to vision using visual descriptions, which are text-based descriptions of visual attributes. We hypothesize that these descriptions can enhance the grounding of concepts to vision, thereby improving performance in vision-language tasks....