On the Interaction with Large Language Models for Web Accessibility: Implications and Challenges

Delnevo, Giovanni; Andruccioli, Manuel; Mirri, Silvia

doi:10.1109/ccnc51664.2024.10454680

2024 IEEE 21st Consumer Communications &Amp; Networking Conference (CCNC) 2024

DOI: 10.1109/ccnc51664.2024.10454680

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On the Interaction with Large Language Models for Web Accessibility: Implications and Challenges

Giovanni Delnevo,

Manuel Andruccioli,

Silvia Mirri

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2024

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Integrating Content Moderation Systems with Large Language Models

Franco,

Gaggi,

Palazzi

2024

ACM Trans. Web

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Online Social Networks (OSNs) rely on content moderation systems to ensure platform and user safety by preventing malicious activities, like the spread of harmful content. However, there is a growing consensus suggesting that such systems are unfair to historically marginalized individuals, fragile users, and minorities. Additionally, OSN policies are often hardcoded in AI-based violation classifiers, making personalized content moderation challenging. In addition, there is a need for more communication between users and platform administrators, especially in case of disagreement about a moderation decision. To address these issues, we propose integrating content moderation systems with Large Language Models (LLMs) to enhance support for personal content moderation and improve user-platform communication. We also evaluate the content moderation capabilities of GPT 3.5 and LLaMa 2, comparing them to commercial products, as well as discuss the limitations of our approach and the open research directions.

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Integrating Content Moderation Systems with Large Language Models

Franco,

Gaggi,

Palazzi

2024

ACM Trans. Web

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show abstract

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On the Interaction with Large Language Models for Web Accessibility: Implications and Challenges

Cited by 1 publication

References 17 publications

Integrating Content Moderation Systems with Large Language Models

Integrating Content Moderation Systems with Large Language Models

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