Towards a Safer Conversation Space: Detection of Toxic Content in Social Media (Student Consortium)

Sahana, B S; Sandhya, G.; Tanuja, R; Ellur, Sushma; Ajina, A.

doi:10.1109/bigmm50055.2020.00052

Cited by 7 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, researchers have utilized masked language modelling techniques to assess a model's ability to detect toxicity given a prompt and a template question [56], with findings indicating the superiority of T5 over GPT-2 in this regard. Nevertheless, it has been recognized that a widely adopted strategy for achieving improved performance entails the utilization of ensemble methods [53,57,77,129].…”

Section: Toxicity At Sentence Level Classificationmentioning

confidence: 99%

“…• Back translation, involving the translation of a sentence to another language and then back to the original one [53,99]. It is imperative to conduct intensive analysis with this technique, as observed, since toxicity levels may fluctuate [197].…”

Section: Data Augmentation Techniques For Toxicity Detectionmentioning

confidence: 99%

“…DeBERTa [250] [123, 179] GPT2 [251] [56, 147] T5 [252] [4, 56, 67, 126, 169, 185] BiGRU+LSTM [57,58] BiGRU+CNN+Attention [53,57,58] BERTweet [253] [143, 146] BiGRU+CNN [60,122] XLNet [254] [60, 90, 146, 154, 157, 169, 176, 216] XLM-R [255] [82, 159, 169, 195, 196] DGPT [256] [61]…”

Section: Model Namementioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Toxicity in Large Language Models: Definitions, Datasets, Detectors, Detoxification Methods and Challenges

Villate-Castillo,

Ser,

Urquijo

2024

Preprint

View full text Add to dashboard Cite

The emergence of the transformer architecture has ushered in a new era of possibilities, showcasing remarkable capabilities in generative tasks exemplified by models like GPT4o, Claude 3, and Llama 3. However, these advancements come with a caveat: predominantly trained on data gleaned from social media platforms, these systems inadvertently perpetuate societal biases and toxicity. Recognizing the paramount importance of AI Safety and Alignment, our study embarks on a thorough exploration through a comprehensive literature review focused on toxic language. Delving into various definitions, detection methodologies, and mitigation strategies, we aim to shed light on the complexities of this issue. While our focus primarily centres on transformer-based architectures, we also acknowledge and incorporate existing research within the realm of deep learning. Through our investigation, we uncover a multitude of challenges inherent in toxicity mitigation and detection models. These challenges range from inherent biases and generalization issues to the necessity for standardized definitions of toxic language and the quality assurance of dataset annotations. Furthermore, we emphasize the significance of transparent annotation processes, resolution of annotation disagreements, and the enhancement of Large Language Models (LLMs) robustness. Additionally, we advocate for the creation of standardized benchmarks to gauge the effectiveness of toxicity mitigation and detection methods. Addressing these challenges is not just imperative, but pivotal in advancing the development of safer and more ethically aligned AI systems.

show abstract