One Computer Science MSc thesis to be presented on 14 May

Wednesday, 14 May there will be a master thesis presentation in Computer Science at Lund University, Faculty of Engineering.

The presentation will take place in E:4130 (Lucas).

Note to potential opponents: Register as an opponent to the presentation of your choice by sending an email to the examiner for that presentation (firstname.lastname@cs.lth.se). Do not forget to specify the presentation you register for! Note that the number of opponents may be limited (often to two), so you might be forced to choose another presentation if you register too late. Registrations are individual, just as the oppositions are! More instructions are found on this page.

15:15-16:00 in E:4130 (Lucas) N.B No more opponents for this presentation

Presenters: Rasmus Olsson, Nils Broman
Title: Transparent Predictions: Visualizing Mental Health Model Reasoning
Examiner: Jacek Malec
Supervisors: Volker Krüger (LTH), Oscar Kjell (Minding Health)

This thesis investigates how embeddings from different pre-trained large language models (LLMs) influence the prediction of mental health rating scale scores from free-text input, and examines how well the models’ underlying word- and sentence-level attributions align with expert judgments. We compare three LLMs--BERT, RoBERTa, and Mixedbread—in their ability to predict PHQ-9 depression scores and highlight important text components. A multi-level visualization tool was reconstructed and extended to make model predictions more transparent at the word, sentence, and paragraph levels. Our results show that larger, more specialized models like Mixedbread yield stronger correlations with expert ratings, both in paragraph-level predictions and sentence-level contribution rankings. However, alignment at the word level was limited, indicating that further refinement in modeling or annotation strategies may be needed to capture fine-grained linguistic cues. This work highlights both the promise and current limitations of using LLMs in clinical contexts, and proposes directions for building interpretable, NLP-based tools to support mental health assessment.

Link to popular science summary: To be uploaded