The importance of visual modelling languages in generative software engineering

2024-11-27 · Roberto Rossi

Multimodal GPTs represent a watershed in the interplay between Software Engineering and Generative Artificial Intelligence. GPT-4 accepts image and text inputs, rather than simply natural language. We investigate relevant use cases stemming from these enhanced capabilities of GPT-4. To the best of our knowledge, no other work has investigated similar use cases involving Software Engineering tasks carried out via multimodal GPTs prompted with a mix of diagrams and natural language.

📄 PDF Abstract BibTeX arXiv:2411.17976

Code (1)

gwr3n/gense 공식 구현

Methods 이 논문이 사용한 방법론

Attention 설명 없음

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Adam 설명 없음

Predicting Good Configurations for GitHub and Stack Overflow Topic Models

2018-04-13 · Christoph Treude, Markus Wagner

Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow. To make sense of this textual data, topic model…

Feature Importance Topic Models

Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks

2018-03-09 · Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes

Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these. To address this limitation, we propose a novel saliency estimation model …

Saliency Prediction

BabyLMs for isiXhosa: Data-Efficient Language Modelling in a Low-Resource Context

2025-01-07 · Alexis Matzopoulos, Charl Hendriks, Hishaam Mahomed, Francois Meyer

The BabyLM challenge called on participants to develop sample-efficient language models. Submissions were pretrained on a fixed English corpus, limited to the amount of words children are exposed to in development (<100m…

Language Modelling NER POS POS Tagging+1

Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review

2025-05-07 · Josh McGiff, Nikola S. Nikolov

Generative language modelling has surged in popularity with the emergence of services such as ChatGPT and Google Gemini. While these models have demonstrated transformative potential in productivity and communication, th…

Data Augmentation Language Modelling Prompt Engineering

Large-Scale Contextualised Language Modelling for Norwegian

2021-04-13 · NoDaLiDa 2021 5 · Andrey Kutuzov, Jeremy Barnes, Erik Velldal, Lilja Øvrelid 외

We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment,…

Language Modelling