Pretrained language models, particularly large language models (LLMs) like ChatGPT,
have achieved significant success across various NLP tasks. However, there is considerable
evidence that these models tend to adopt the cultural biases inherent in the datasets used for
training, thereby unintentionally reinforcing biased patterns and potentially causing harm.
This thesis aims to investigate these biases across different categories, such as racial
and gender biases, in both Estonian and English language models.
[1]
E. Kaukonen, A. Sabir, R. Sharma, How Aunt-Like Are You? Exploring Gender Bias in the Genderless Estonian Language: A Case Study, (2025 Nodalida)
Pretrained vision-language models (VLMs) like CLIP exhibit impressive performance in aligning images with language.
However, their behavior often reflects biases present in training data, affecting how they ground language in
visual inputs. This thesis examines how VLMs interpret objects and images through textual prompts, focusing
on the dynamics of language grounding and the emergence of bias in multimodal predictions.
[2]
Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Grounding Language Models to Images for Multimodal Inputs and Outputs (2023 ICML)