Development of an Adaptive User Support System Based on Multimodal Large Language Models

As software systems become more complex, some users find it challenging to use these tools efficiently, leading to frustration and decreased productivity. We tackle the shortcomings of conventional user support mechanisms in software and aim to create and assess a user support system that integrates Multimodal Large Language Models (MLLMs) for producing support messages. Our system initially segments the user interface to serve as a reference for selection and requests users to specify their preferences for support messages. Following this, the system creates personalised user support messages for each individual. We propose that user support systems enhanced with MLLMs can provide more efficient and bespoke assistance compared to conventional methods.

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Publication Details

Published
2024-09-02
DOI
https://doi.org/10.1109/vl/hcc60511.2024.00044
Citations
1
Primary Topic
Engineering Education and Technology
Type
article
Field-Weighted Citation Impact
0.78
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Development of an Adaptive User Support System Based on Multimodal Large Language Models

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Development of an Adaptive User Support System Based on Multimodal Large Language Models

Shavindra Wickramathilaka, Hourieh Khalajzadeh, Anuradha Madugalla, John Grundy, Humphrey O. Obie, Lin Li, Wei Wang
article en
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Abstract

As software systems become more complex, some users find it challenging to use these tools efficiently, leading to frustration and decreased productivity. We tackle the shortcomings of conventional user support mechanisms in software and aim to create and assess a user support system that integrates Multimodal Large Language Models (MLLMs) for producing support messages. Our system initially segments the user interface to serve as a reference for selection and requests users to specify their preferences for support messages. Following this, the system creates personalised user support messages for each individual. We propose that user support systems enhanced with MLLMs can provide more efficient and bespoke assistance compared to conventional methods.

Deakin University (AU), Monash University (AU), RMIT University (AU)
Openalex Percentile: Top 20%
Engineering Education and Technology
0.78
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