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Systematic comparison of encoder vs. decoder safety judges for LLM adversarial evaluation
A new arXiv preprint evaluates whether fine-tuned encoder classifiers from the ModernBERT family (ModernBERT and Ettin) can replace LLM-based safety judges for detecting harmful outputs in user-model conversations. The study benchmarks encoders against rule-based methods, fine-tuned LLM classifiers, and LLM judges including LlamaGuard 3/4, ShieldGemma, StrongReject, and Claude-as-a-judge across multiple adversarial attack types. Results are reported on F1, false negative rate, and precision-recall, with breakdowns by attack technique, providing practical guidance on cost-latency tradeoffs for production safety pipelines.
LOCUS: A large-scale corpus of U.S. local ordinances for legal AI research
Researchers introduce LOCUS, a comprehensive machine-readable corpus of U.S. municipal and county ordinance codes covering 9,239 jurisdictions, with a county-harmonized access layer spanning 2,309 of 3,144 U.S. counties. The corpus was assembled using OCR to handle diverse document formats previously locked in vendor platforms, and is released on HuggingFace alongside ModernBERT-based classifiers for analyzing local law along dimensions like opacity and paternalism. The work addresses a significant gap in legal AI training data, as local ordinances govern large swaths of everyday regulation but have been absent from existing corpora.
ACL-Verbatim: Hallucination-Free Extractive QA System for Research Papers
The paper introduces ACL-Verbatim, an extractive question answering system built on VerbatimRAG that maps user queries directly to verbatim text spans in ACL Anthology papers, eliminating hallucination by design. The authors contribute a new ground-truth benchmark dataset created via human NLP-researcher annotation over synthetic queries generated using a ScIRGen-based pipeline. A 150M-parameter ModernBERT token classifier trained on silver supervision achieves the best word-level F1 of 53.6, outperforming the strongest LLM-based extractor at 48.7. The work demonstrates that smaller extractive models can outperform large generative LLMs on precision-critical retrieval tasks.
Finally, a Replacement for BERT: Introducing ModernBERT
Hugging Face introduces ModernBERT, a modernized encoder-only transformer model designed as a successor to BERT. The model incorporates architectural improvements developed since BERT's 2018 release, targeting better performance on downstream NLP tasks. ModernBERT aims to fill the gap for efficient encoder models in retrieval, classification, and other discriminative tasks where decoder-only LLMs are often overkill.
mmBERT: ModernBERT goes Multilingual
Hugging Face introduces mmBERT, a multilingual extension of ModernBERT. The post describes adapting the ModernBERT architecture for multilingual text encoding tasks. This represents an incremental but meaningful expansion of the ModernBERT family to cover non-English languages.