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Found 8 papers, 2 papers with code
ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations
Detecting hate speech and offensive language is essential for maintaining a safe and respectful digital environment.
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
This study conducts a comprehensive review and analysis of the existing literature on the attitudes of LLMs towards the 17 SDGs, emphasizing the comparison between their attitudes and support for each goal and those of humans.
Chinese Offensive Language Detection:Current Status and Future Directions
Despite the considerable efforts being made to monitor and regulate user-generated content on social media platforms, the pervasiveness of offensive language, such as hate speech or cyberbullying, in the digital space remains a significant challenge.
Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs
We find that GPT-4 performs best on the task, followed by GPT-3. 5, but that the open source language models are also able to perform it and that the 7B parameter Mistral displays as little difference between its baseline performance on the natural language inference task and the non-prototypical syntactic category task, as the massive GPT-4.
Saving the legacy of Hero Ibash: Evaluating Four Language Models for Aminoacian
This study assesses four cutting-edge language models in the underexplored Aminoacian language.
Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection
The spread of disinformation and propagandistic content poses a threat to societal harmony, undermining informed decision-making and trust in reliable sources.
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80. 7%.
Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function
By fitting a 3D morphable model from multi-view images, the features of multiple images are extracted and aggregated in the mesh-attached UV space, which makes the implicit function more effective in recovering detailed facial shape.
