The substantial amount of tabular data can be attributed to its storage convenience. There is a high demand for learning useful information from the data. To achieve that, machine learning models, called transformers, have been created. They can find patterns in the data, learn from them, and improve their predictive abilities based on that learning experience. There are also tabular transformers for tabular data. In order to attempt to increase the predictive performance of the transformers, we have combined them with graph neural networks (GNNs), which are again machine learning models, which work on graph data by learning information from the nodes and the edges. A graph representation of the dataset is created and input into the graph neural network. The architecture that fuses these two machine learning models is a more complex machine learning model that combines the transformer and the GNN. The aim is to increase the predictive ability of the model for values from the table or to predict whether an edge in the graph exists, which represents whether a transaction between two users exists. We have built the architecture using certain types of a tabular transformer and a graph neural network, FT-Transformer and GINe respectively, and the next step is to try modifying this architecture by using different models, and different ways of using these layers, for example how many copies we are creating of it. This has the potential to be a versatile model than can be used for different kinds of datasets. We have seen notable improvement in performance when using a different GNN, PNA. The transformer ResNet also shows to be on a similar or slightly better performing level than FT-Transformer when not combined with a GNN. GraphSage in the fused model underperforms significantly due to its weakness to capture simple graph structures.

Deep Learning models can use pretext tasks to learn representations on unlabelled datasets. Although there have been several works on representation learning and pre-training, to the best of our knowledge combining pretext tasks in a multi-task setting for relational multimodal data has not been done before. In this work, we implemented 4 pretext tasks on top of a framework for handling relational multi-modal data and evaluated them based on two datasets. We first identified the best-performing masking strategy for pretext tasks that require masking. Then, we compared different combinations of the pretext tasks based on self-supervised metrics as a proxy for the quality of the representation learned. The results reveal that masking values by replacing from the column's empirical distribution yields 4.6\% and 4\% higher accuracy on each dataset respectively than replacing them with a fixed value. We also found that different combinations of pretext tasks, even with different numbers of tasks, converge to marginally different values and MoCo further reduces this difference. Our findings imply that the number of pretext tasks can scale efficiently allowing for a more diverse representation to be learned.

While LLMs are proficient in processing textual information, integrating them with other models presents significant challenges.
This study evaluates the effectiveness of various configurations for integrating a large language model (LLM) with models capable of handling multimodal data.\\

We explore the advantages of using pre-trained LLMs for generating text embeddings and the benefits of fine-tuning LLMs for specific tasks. Our investigation includes various fine-tuning strategies, such as Low-Rank Adaptation (LoRA), prompt tuning, and full fine-tuning, applied to both smaller and larger language models. Additionally, we analyze different training setups, including sequential and cascaded training of LLMs and downstream architectures. Our comparative analysis evaluates the performance and cost-effectiveness of these methods. The findings indicate that while full fine-tuning achieves the best results, LoRA offers a practical balance between computational efficiency and model performance. We also highlight the correlation between increased LLM size and corresponding increases in cost and performance.

With the increase of machine learning applications in our every-day life, high-quality datasets are becoming necessary to train accurate and reliable models. This research delves into the factors that contribute to a high quality dataset and examines how different dataset metrics affect the performance of machine learning models particularly focusing on Graph Neural Networks (GNNs) Tabular Transformers and Large Language Models (LLMs). The metrics, under scrutiny include graph sparsity, missing data cells, modularity and text length. Various datasets are adjusted to assess how these metrics impact model performance.

The results of the experiments reveal that sparse graphs can preserve relational information. However increasing density does not necessarily lead to improved performance due to noise interference. The models demonstrated accuracy and low error rates in the presence of significant missing data indicating their ability to handle incomplete information effectively and generalize well based on imputation strategies and structural design. Higher modularity was found to aid in capturing patterns. Introduced complexity that could potentially hinder performance. Notably text length emerged as a factor influencing model performance by offering contextual details.

These insights show the significance of considering attributes when designing machine learning models for intricate predictive tasks. Through experimentation and optimization of these metrics we can enhance model resilience and accuracy for applicability, in real world scenarios.