更安全LLM时代的Hatebench：可复现性研究

Computer Science > Emerging Technologies arXiv:2609.05169 (cs) [Submitted on 4 Sep 2026] Title:Hatebench in the era of safer LLMs Authors:Ole Becker, Tobias Jongen, Philip Kolbe, Sonal Khosla, Vaibhav Bajpai View a PDF of the paper titled Hatebench in the era of safer LLMs, by Ole Becker and 3 other authors 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. Subjects: Emerging Technologies (cs.ET) Cite as: arXiv:2609.05169 [cs.ET] (or arXiv:2609.05169v1 [cs.ET] for this version) https://doi.org/10.48550/arXiv.2609.05169 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sonal Khosla Dr [view email] [v1] Fri, 4 Sep 2026 14:11:21 UTC (228 KB) Full-text links: Access Paper: View a PDF of the paper titled Hatebench in the era of safer LLMs, by Ole Becker and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.ET new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs 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. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)