Hatebench in the era of safer LLMs

Computer Science > Emerging Technologies [Submitted on 4 Sep 2026] Title:Hatebench in the era of safer LLMs View PDF HTML (experimental)Abstract:As Large Language Models (LLMs) lower the barrier for au- tomated content generation, the potential for producing hate speech poses a significant challenge for digital safety. This paper presents a reproducibility study of the HateBench paper by Shen et al., investigating whether existing hate speech detectors, typically trained on human-authored data, generalize to LLM-generated hateful content, and evaluating whether their reported weaknesses are stable over time and robust to evolving components. We independently reconstruct the original dataset genera- tion pipeline using modern LLMs and extend the benchmark to include recently released models and updated detector versions. Our independent assessment under current con- ditions finds that for newer LLMs, safeguards have been put into place to prevent the generation of harmful content. We also replicate the results for two sophisticated types of hate campaigns. While the original findings seem to have been overestimated slightly due to bias in the datasets, the overall findings can be confirmed. Finally, we compare text- Moderation against the newer omni-Moderation and find that its robustness against adversarial hate campaigns has improved slightly. By clarifying which detector vulnerabil- ities persist, this study informs the community about the longevity of content moderation measurements. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.