Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Augmenting Dark Patterns Text Data by Leveraging Large Language Models: A Multi-agent Framework and Parameter-Efficient Fine-Tuning
KOCYIGIT, Emre; LIGA, Davide; LENZINI, Gabriele
2025 • In Mualla, Yazan (Ed.) Advances in Explainability, Agents, and Large Language Models - 1st International Workshop on Causality, Agents and Large Models, CALM 2024, Proceedings
dark patterns; data augmentation; large language models; llama3; parameter-efficient fine-tuning; Agent parameters; Dark pattern; Data augmentation; Fine tuning; Language model; Large language model; Llama3; Multiagent framework; Parameter-efficient fine-tuning; Text data; Computer Science (all); Mathematics (all)
Abstract :
[en] Dark patterns, i.e., deceptive design patterns that employ manipulative strategies to deceive or force online users to take a decision against their interests, are widely available in online world. However, the variety and quantity of structured and labelled dark pattern datasets, which are critical for automated dark pattern detection, particularly for AI-based detection models, are limited. In this study, we leverage Large Language Models’ (LLM) sophisticated text data generation ability and propose a dark pattern text data augmentation method by utilizing a state of art open source language model and multi-agents framework, which has generator and controller models. Evaluation of the augmentation demonstrates that while increasing the data size, our proposal-based augmented data preserves the same dark pattern characteristics of the source data and maintains its diversity. We set forth that dark pattern text data can be generated even based on a few examples via prompt engineering techniques on the LLMs. We also show that our augmented data can be used to fine-tune pre-trained language models using Low-Rank Adaptation to enhance their robustness in detecting dark patterns.
Disciplines :
Computer science
Author, co-author :
KOCYIGIT, Emre ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
LIGA, Davide ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
LENZINI, Gabriele ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > IRiSC
External co-authors :
no
Language :
English
Title :
Augmenting Dark Patterns Text Data by Leveraging Large Language Models: A Multi-agent Framework and Parameter-Efficient Fine-Tuning
Publication date :
25 April 2025
Event name :
International Workshop on Causality, Agents and Large Models
Event place :
Kyoto, Japan
Event date :
18-11-2024 => 19-11-2024
Main work title :
Advances in Explainability, Agents, and Large Language Models - 1st International Workshop on Causality, Agents and Large Models, CALM 2024, Proceedings
Editor :
Mualla, Yazan
Publisher :
Springer Science and Business Media Deutschland GmbH
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