REVIEW 4 cited by
ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Ensuring the safety of large language models (LLMs) is critical as they are deployed in real-world applications. Existing guardrails rely on rule-based filtering or single-pass classification, limiting their ability to handle nuanced safety violations. To address this, we propose ThinkGuard, a critique-augmented guardrail model that distills knowledge from high-capacity LLMs by generating structured critiques alongside safety labels. Fine-tuned on critique-augmented data, the captured deliberative thinking ability drastically enhances the guardrail's cautiousness and interpretability. Evaluated on multiple safety benchmarks, ThinkGuard achieves the highest average F1 and AUPRC, outperforming all baselines. Compared to LLaMA Guard 3, ThinkGuard improves accuracy by 16.1% and macro F1 by 27.0%. Moreover, it surpasses label-only fine-tuned models, confirming that structured critiques enhance both classification precision and nuanced safety reasoning while maintaining computational efficiency.
Forward citations
Cited by 4 Pith papers
-
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs
A multi-turn red-teaming agent trained on simulated attacker-defender conversations induces vulnerable code at higher rates than prior attack methods across several code LLMs.
-
Beyond Safe Answers: A Benchmark for Evaluating True Risk Awareness in Large Reasoning Models
A new benchmark shows that top reasoning models identify all relevant risks in under 40% of cases even when their final answers look safe.
-
ShieldVLM: Safeguarding the Multimodal Implicit Toxicity via Deliberative Reasoning with LVLMs
ShieldVLM detects multimodal implicit toxicity through deliberate cross-modal reasoning, outperforming existing moderation APIs and models on the new MMIT benchmark.
-
From Rogue to Safe AI: The Role of Explicit Refusals in Aligning LLMs with International Humanitarian Law
Across eight LLMs, most explicitly IHL-violating prompts are refused, and a single system-level safety prompt raises explanatory refusal rates in six of eight models, though the benchmark is not publicly released.
Discussion (0). Continue with ORCID to comment.