Keeping Users Engaged During Repeated Administration of the Same Questionnaire: Using Large Language Models to Reliably Diversify Questions

Standardized, validated questionnaires are vital tools in HCI research and healthcare, offering dependable self-report data. However, their repeated use in longitudinal or pre-post studies can induce respondent fatigue, impacting data quality via response biases and decreased response rates. We propose utilizing large language models (LLMs) to generate diverse questionnaire versions while retaining good psychometric properties. In a longitudinal study, participants engaged with our agent system and responded daily for two weeks to either a standardized depression questionnaire or one of two LLM-generated questionnaire variants, alongside a validated depression questionnaire. Psychometric testing revealed consistent covariation between the external criterion and the focal measure administered across the three conditions, demonstrating the reliability and validity of the LLM-generated variants. Participants found the repeated administration of the standardized questionnaire significantly more repetitive compared to the variants. Our findings highlight the potential of LLM-generated variants to invigorate questionnaires, fostering engagement and interest without compromising validity.

TEASEL: A Transformer-Based Speech-Prefixed Language Model

Multimodal language analysis is a burgeoning field of NLP that aims to simultaneously model a speaker's words, acoustical annotations, and facial expressions. In this area, lexicon features usually outperform other modalities because they are pre-trained on large corpora via Transformer-based models. Despite their strong performance, training a new self-supervised learning (SSL) Transformer on any modality is not usually attainable due to insufficient data, which is the case in multimodal language learning. This work proposes a Transformer-Based Speech-Prefixed Language Model called TEASEL to approach the mentioned constraints without training a complete Transformer model. TEASEL model includes speech modality as a dynamic prefix besides the textual modality compared to a conventional language model. This method exploits a conventional pre-trained language model as a cross-modal Transformer model. We evaluated TEASEL for the multimodal sentiment analysis task defined by CMU-MOSI dataset. Extensive experiments show that our model outperforms unimodal baseline language models by 4% and outperforms the current multimodal state-of-the-art (SoTA) model by 1% in F1-score. Additionally, our proposed method is 72% smaller than the SoTA model.