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DeBERTa-v3
modelactive
deberta-v3-12660b04·5 events·first seen Aliases: DeBERTa-v3, DeBERTa-v2, DeBERTa-v3-large, DeBERTa-v3-base
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Recent events (5)
Schwartz-Geometry Decoding improves coherence in human value detection without sacrificing F1
A new arXiv preprint proposes injecting the circular Schwartz value continuum as an output-space geometry into multi-label classifiers for human value detection. The authors compare training-time geometry-aware objectives against a post-hoc energy decoder on a DeBERTa-v3-base model, finding that the decoder improves label-set coherence with the theoretical continuum without degrading Macro-F1 or Micro-F1. Training-time geometry injection yields only marginal gains, no better than a random ordering. A Qwen2.5-72B-Instruct diagnostic shows that supplying the continuum at inference shifts behavior but does not match supervised structured prediction.
CCG directed types improve structural generalization on SLOG, surpassing AM-Parser on position-shift categories
A new arXiv preprint redesigns the symbolic backend of a semantic parsing system using CCG directed types with a deterministic CKY decoder and only 30K learnable parameters, achieving 75.9% LF exact match on the SLOG benchmark under BERT-base, surpassing the previous SOTA AM-Parser (70.8%). Gains are highly category-specific: the CCG system outperforms AM-Parser on all 5 position-shift categories (+29.9pp) while AM-Parser retains an edge on recursive-depth categories. Swapping in DeBERTa-v3-large as encoder pushes performance to 90.7%, with encoder gains complementing directionality gains in orthogonal category groupings. The work argues that directional representations shift the generalization bottleneck from the symbolic layer to the neural encoder, enabling further improvement through encoder scaling.
Meta releases Llama Prompt Guard 2 (22M) safety classifier on Hugging Face
Meta released Llama Prompt Guard 2-22M, a lightweight 22-million-parameter text classification model for prompt safety, published on Hugging Face under the meta-llama organization. The model is based on DeBERTa-v2 architecture and tagged for safety use cases including prompt injection and jailbreak detection. It is part of the Llama 4 safety tooling ecosystem and supports English and French.
Meta releases Llama Prompt Guard 2 (86M) for prompt injection and jailbreak detection
Meta released Llama Prompt Guard 2-86M, a DeBERTa-v2-based text classification model on Hugging Face designed for safety filtering, specifically prompt injection and jailbreak detection. The model is tagged with llama4, suggesting it is part of the Llama 4 safety tooling ecosystem. With over 122K downloads, it has seen meaningful early adoption.
Systematic Study of Schwartz Value Detection in Political Texts: Context, Scale, and Moral Knowledge
This paper investigates when additional context, larger models, or retrieved moral knowledge improve detection of Schwartz human values in political text using the ValueEval benchmark format. Key findings show that full-document context helps supervised DeBERTa encoders (+3.8–4.8 macro-F1) but not zero-shot LLMs, while RAG with a curated moral knowledge base consistently benefits all model families under early fusion. Scaling model size does not guarantee gains, and simple early fusion outperforms more complex RAG variants. The study recommends jointly evaluating context, knowledge, and model family rather than assuming larger inputs or models universally improve value-sensitive NLP.