Large Language Models in Banking: Lessons from a Low Resource Language Region

This thesis investigates the integration of Large Language Models (LLMs) within large organizations operating in low resource language (LRL) regions, with a particular focus on Íslandsbanki, one of the prominent banks in Iceland currently grappling with the adoption of LLM technology. The research aims to understand the frameworks governing LLM...

Augmenting Program Synthesis with Large Language Models: Incorporating Natural Language Understanding for Efficient Program Synthesis

This research introduces a Language Model Augmented Program Synthesis (LMAPS) workflow to enhance traditional Programming by Example (PBE). PBE is a method to automatically generate a program that satisfies a specification that consists of a set of input-output examples. These program specifications are often defined by a few examples, which can...

Evaluating the effectiveness of large language models in meeting summarization with transcript segmentation techniques: How well does gpt-3.5-turbo perform on meeting summarization with topic and context-length window segmentation?

Large Language Models (LLM) have brought significant performance increase on many Natural Language Processing tasks. However LLMs have not been tested for meeting summarization. This research paper examines the effectiveness of the gpt-3.5-turbo model on the meeting summarization domain. However due to input length limitations, it cannot be...

Evaluating Large Language Model Performance on User and Language Defined Elements in Code

Large Language Models of code have seen significant jumps in performance recently. However, these jumps tend to accompany a notable and perhaps concerning increase in scale and costs. We contribute an evaluation of prediction performance with respect to model size by assessing the layer-wise progression for language and user-defined elements in...

Use of AI-driven code generation models in teaching and learning programming: a systematic literature review

The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and...

CodeGPT on XTC: Compressing a CodeGPT Model Using Hybrid Layer Reduction and Extreme Quantisation through Knowledge Distillation

Large language models are powerful because of their state-of-the-art language processing abilities. But, they come at the cost of being extremely resource-intensive, and are steadily growing in size. As a result, compressing such models for resource- constrained devices is an active and promising re- search area. In spite of their current...

Empirical Study on Test Generation Using GitHub Copilot

Writing unit tests is a crucial task in the software development lifecycle, ensuring the correctness of the software developed. Due to its time-consuming and laborious nature, it is, however, often neglected by software engineers. Numerous automatic test generation tools have been devised to ease unit testing efforts, but these test generation...