Search Results for author:
Found 14 papers, 7 papers with code
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation.
Demystifying Hateful Content: Leveraging Large Multimodal Models for Hateful Meme Detection with Explainable Decisions
In this paper, we introduce IntMeme, a novel framework that leverages Large Multimodal Models (LMMs) for hateful meme classification with explainable decisions.
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning
The widespread presence of hate speech on the internet, including formats such as text-based tweets and vision-language memes, poses a significant challenge to digital platform safety.
LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs
Current benchmarks like Needle-in-a-Haystack (NIAH), Ruler, and Needlebench focus on models' ability to understand long-context input sequences but fail to capture a critical dimension: the generation of high-quality long-form text.
SGHateCheck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of Singapore
To address the limitations of current hate speech detection models, we introduce \textsf{SGHateCheck}, a novel framework designed for the linguistic and cultural context of Singapore and Southeast Asia.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content.
Prompting Large Language Models for Topic Modeling
Topic modeling is a widely used technique for revealing underlying thematic structures within textual data.
MATK: The Meme Analytical Tool Kit
The rise of social media platforms has brought about a new digital culture called memes.
PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models
The proliferation of social media has given rise to a new form of communication: memes.
Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection
Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection.
Ranked #13 on
Meme Classification
on Hateful Memes
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore
While several countries have adopted total defence as their defence policy, very few studies have investigated its effectiveness.
Evaluating GPT-3 Generated Explanations for Hateful Content Moderation
A key concern is that these explanations, generated by LLMs, may lead to erroneous judgments about the nature of flagged content by both users and content moderators.
Decoding the Underlying Meaning of Multimodal Hateful Memes
Recent studies have proposed models that yielded promising performance for the hateful meme classification task.
On Explaining Multimodal Hateful Meme Detection Models
For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i. e., image and text) of the hateful memes.
