RAGognizer adds a detection head to LLMs for joint training on generation and token-level hallucination detection, yielding SOTA detection and fewer hallucinations in RAG while preserving output quality.
Qwen Team
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
Benign fine-tuning collapses safety geometry in guard models like Granite Guardian, dropping refusal to 0%, but Fisher-Weighted Safety Subspace Regularization restores it to 75% while improving robustness.
SkillGuard-Robust formulates pre-load auditing of untrusted Agent Skills as a three-way classification task and achieves 97.30% exact match and 98.33% malicious-risk recall on held-out benchmarks.
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
TWGuard achieves +0.289 F1 improvement and 94.9% false-positive reduction for LLM safety guardrails in the Taiwan linguistic context compared to foundation models and baselines.
citing papers explorer
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RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
RAGognizer adds a detection head to LLMs for joint training on generation and token-level hallucination detection, yielding SOTA detection and fewer hallucinations in RAG while preserving output quality.
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When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models
Benign fine-tuning collapses safety geometry in guard models like Granite Guardian, dropping refusal to 0%, but Fisher-Weighted Safety Subspace Regularization restores it to 75% while improving robustness.
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Structured Security Auditing and Robustness Enhancement for Untrusted Agent Skills
SkillGuard-Robust formulates pre-load auditing of untrusted Agent Skills as a three-way classification task and achieves 97.30% exact match and 98.33% malicious-risk recall on held-out benchmarks.
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Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
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TWGuard: A Case Study of LLM Safety Guardrails for Localized Linguistic Contexts
TWGuard achieves +0.289 F1 improvement and 94.9% false-positive reduction for LLM safety guardrails in the Taiwan linguistic context compared to foundation models and baselines.