I believe that as LLMs become increasingly integrated into everyday life, it is important to better understand their social awareness and moral reasoning. Therefore, my research interests are focused on social intelligence, fairness, moral judgment, and behavioral patterns of LLMs in high-stakes social contexts, especially in education. I am interested both in analyzing and improving these capabilities of existing models, as well as in developing LLM-based frameworks that support humans in such domains. I am also interested in these topics in the context of AI Safety and Alignment Research.
Abstract: This project focuses on a holistic, metric-based view of complexity with the aim to enable effective LLM-based complexity adjustments.
Abstract: PATS is a framework for developing personality-aware teaching strategies with large language model tutors. The work explores how different personality traits can be leveraged to create more effective and personalized educational experiences.
Abstract: In this work, we address these limitations for Yakut (Sakha), a Turkic language with Cyrillic orthography, by engineering a Byte Pair Encoding (BPE) tokenizer with Yakut-specific pre- and postprocessing rules and tailored special tokens.
Abstract: This paper proposes a novel deep-learning based solution for Twitter sentiment analysis that addresses the challenges of automatic and noisily labelled data. Leveraging the pre-trained BERTweet model for embeddings, we develop a novel CRNN-based ‘fusion net’ architecture combining CNN, RNN, and Attention layers.
* denotes equal contribution.