Improving Language Models Using Augmentation and Multi-Modality

Licentiatavhandling, 2023

Language models have become a core component in modern Natural Language Processing (NLP) as they constitute a powerful base that is easily adaptable to many language processing tasks. Part of the strength lies in their ability to embed associations representing general world knowledge. However, the associations formed by these models are brittle, even when scaled to huge sizes and using massive amounts of data. This, in combination with other problems such as lack of attributability and high costs, motivate us to investigate other methods to improve on these aspects.

In this thesis, we investigate methods that augment language models with additional contextual information, for the purpose of simplifying the language modeling problem and increasing the formation of desirable associations. We also investigate whether multi-modal data can assist in forming such associations, that could otherwise be difficult to obtain from textual data only.

In our experiments, we showcase augmentation to be effective toward these ends, in both a textual and multi-modal case. We also demonstrate that visual data can assist in forming knowledge-representing associations in a language model.