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Found 8 papers, 2 papers with code
Deciphering Hate: Identifying Hateful Memes and Their Targets
The dataset consists of 7, 148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target (i. e., Individual, Organization, Community, and Society).
Mad Libs Are All You Need: Augmenting Cross-Domain Document-Level Event Argument Data
Our approach leverages the intuition that Mad Libs, which are categorically masked documents used as a part of a popular game, can be generated and solved by LLMs to produce data for DocEAE.
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
We detail (i) a method of claim identification -- the task of identifying if a post title contains a claim and (ii) an opinion mining-driven evaluation framework for stance detection using LLMs.
Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection
Evaluation results demonstrate our proposed approach's effectiveness with F1-scores of $69. 7$% and $70. 3$% for the MUTE and MultiOFF datasets.
Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts
Our error analysis shows that AMR-infused language models perform better on complex texts and generally show less predictive variance in the presence of changing complexity.
Characterizing Information Seeking Events in Health-Related Social Discourse
To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse.
HealthE: Classifying Entities in Online Textual Health Advice
The processing of entities in natural language is essential to many medical NLP systems.
Scope of Pre-trained Language Models for Detecting Conflicting Health Information
This is the task of health conflict detection (HCD).
