FULLTEXT01.pdf

– The paper focuses on classifying toxic output from generative language models.
– It discusses the issues of toxic text generated by GPT-3 and GPT-SWE.
– The study explores the use of toxicity classifiers for filtering Swedish text.
– A small Swedish toxicity dataset is created and used to fine-tune a Swedish BERT model.
– The best performing toxicity classifier is not considered useful in an applied scenario.
– The study highlights the importance of qualitative datasets and expert annotators.
– Potential solutions for toxicity in generative LMs are also discussed.

– The study explores the use of toxicity classifiers for filtering Swedish text generated from GPT-SWE.
– The best performing toxicity classifier created in this work is not considered useful in an applied scenario.
– The study highlights the importance of qualitative datasets for fine-tuning toxicity classifiers.
– Expert annotators, well-defined guidelines, and fine-grained labels are recommended for toxicity annotation.
– Active learning methods can be used to create datasets in languages with lower resources.
– The study provides insights into potential solutions for toxicity in generative LMs.

– The paper explores the use of toxicity classifiers for filtering Swedish text generated from GPT-SWE.
– A small Swedish toxicity dataset is created and annotated for fine-tuning a Swedish BERT model.
– The best performing toxicity classifier created in this work is not useful in an applied scenario.
– The study highlights the importance of qualitative datasets and expert annotators.
– The study provides insights into potential solutions for toxicity in generative LMs.

– Study on creating toxicity classifiers for Swedish text with GPT-SWE.
– Small toxicity dataset and toxicity classifiers based on fine-tuned Swedish BERT models.
– Best performing classifier not useful, but fine-tuned Swedish BERT models show promise.
– Difficulties in toxicity annotation and importance of context.
– Recommendations for future studies on expert annotators and fine-grained labels.
– Future work on improving fine-tuning datasets and investigating domain-specific classifiers.

– The study explores the use of toxicity classifiers to filter Swedish text generated from GPT-SWE.
– A small Swedish toxicity dataset is created and annotated for fine-tuning a Swedish BERT model.
– The best performing toxicity classifier created in this work is not considered useful in an applied scenario.
– The results encourage continued studies on BERT models for creating toxicity classifiers in Swedish.
– The study highlights the importance of qualitative datasets and difficulties of toxicity annotation.
– Expert annotators, well-defined guidelines, and fine-grained labels are recommended.
– The study provides insights into potential solutions for toxicity in generative LMs.

– The study is about a model that generates text in Swedish.
– The model sometimes generates text that is offensive or toxic.
– The study tries to create a separate model to classify toxic text.
– The best performing classifier created in the study is not very useful.
– The study recommends more research and better guidelines for toxicity classification.