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Mekkes, Erik (author)
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...
bachelor thesis 2023
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Keeler, Miranda (author)
We present an investigation into the relationship between the average depth of the first correct prediction and the performance of CodeGen. This was done on a dataset comprised of code files comprised of C++, Go, Java, Julia, Kotlin, and Python. The analysis involved investigating the model's predictions at different layers using a Tuned Lens,...
bachelor thesis 2023
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Kuo, Nadine (author)
The development of contemporary source code auto-completion tools have significantly boosted productivity and efficiency of developers. In 2021, the GPT-2-based Transformer CodeGPT was developed to support code completion and text-to-code generation. Similarly to most code models however, CodeGPT was trained on a limited set of widely-used...
bachelor thesis 2023
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Popescu, Popescu (author)
In recent years, deep learning techniques, particularly transformer models, have demonstrated remarkable advancements in the accuracy and efficiency of language models. These models provide the foundation for many natural language processing tasks, including code completion. The effectiveness of code completion models has been the subject of a...
bachelor thesis 2023
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