Multi Category Prompt Safety Classification

via “zero-shot and few-shot evaluation mode switching”

11K safety evaluation questions across 7 categories.

Unique: Provides curated few-shot examples stratified by safety category (5 per category) rather than random sampling, ensuring balanced representation of each harm type. Prompt templates are explicitly customizable per model (e.g., evaluate_baichuan.py shows Baichuan-specific extraction logic), acknowledging that different architectures require different prompting strategies.

vs others: More systematic than ad-hoc few-shot selection; category-stratified examples ensure consistent coverage of all safety dimensions rather than potentially biased random sampling.

via “7-category safety taxonomy with fine-grained failure mode classification”

11K safety evaluation questions across 7 categories.

Unique: Implements 7-category safety taxonomy with category-balanced few-shot examples enabling systematic failure mode diagnosis. Most safety benchmarks (TruthfulQA, HarmBench) report only aggregate safety scores without category-level breakdown or category-specific few-shot examples.

vs others: Category stratification reveals which safety domains models struggle with, enabling targeted improvements; category-balanced few-shot examples support category-specific evaluation unlike benchmarks with random few-shot sampling.

via “multi-class prompt harmfulness classification”

Allen AI's safety classification dataset and model.

Unique: Trained on WildGuard's curated dataset of 10K+ adversarial prompts spanning 13 harm categories with human annotations, using a multi-task learning approach that jointly optimizes for prompt harmfulness, response harmfulness, and refusal detection — enabling a single model to handle three safety dimensions rather than separate classifiers

vs others: More comprehensive than OpenAI's moderation API (covers more harm categories) and more specialized than generic text classifiers because it's specifically fine-tuned on jailbreak and adversarial prompt patterns rather than general toxicity

via “text classification and sentiment analysis via prompt-based inference”

text-generation model by undefined. 51,86,179 downloads.

Unique: Qwen3-1.7B performs classification through prompt-based generation rather than dedicated classification heads, enabling flexible zero-shot classification without model retraining. The approach trades accuracy for flexibility and ease of deployment.

vs others: More flexible than fine-tuned classifiers for changing category sets; faster inference than ensemble classifiers; lower accuracy than task-specific models but sufficient for many production use cases.

via “classification-specific prompt optimization with categorical evaluation”

Automated prompt engineering. It generates, tests, and ranks prompts to find the best ones.

Unique: Specializes the generic optimization pipeline for classification by replacing pairwise comparisons with classification-specific metrics (accuracy, F1, precision, recall). Includes custom output parsing logic to extract categories from model outputs.

vs others: More precise than generic pairwise comparison for classification because it uses task-specific metrics; more practical than manual evaluation because it automates metric computation across all candidates.

via “prompt-categorization-and-tagging”

| [prompts.csv](prompts.csv) |

Unique: Uses a curated, fixed taxonomy for prompt organization rather than dynamic tagging or user-generated categories, ensuring consistency and discoverability at the cost of flexibility

vs others: More organized and browsable than flat prompt lists, but less flexible than community-driven tagging systems like those in Hugging Face Model Hub

via “multi-category prompt safety classification”

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification)...

Unique: Purpose-built safety classifier based on Llama 3.1 8B (not a general-purpose LLM repurposed for safety) with fine-tuning specifically on safety classification tasks, enabling better calibration of confidence scores and category-specific accuracy compared to using general LLMs with safety prompts

vs others: Smaller and faster than OpenAI Moderation API (8B vs 175B+) while maintaining comparable accuracy on standard safety categories, and can run locally without API latency or cost-per-request fees

via “multi-label safety classification with confidence scoring”

gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust...

Unique: Trained with multi-task learning across safety dimensions, with MoE experts specialized for different harm categories (toxicity experts, hate speech experts, misinformation experts, etc.). Each expert produces independent confidence scores rather than a single aggregated decision.

vs others: More flexible than binary safe/unsafe classifiers because it provides per-category scores, enabling policy-specific thresholds. More interpretable than black-box LLM judges because each label has explicit confidence, supporting audit and appeals workflows

via “taxonomy-based unsafe content categorization”

Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...

Unique: Uses instruction-tuned fine-tuning on safety-labeled data to produce multi-dimensional category scores in a single forward pass, rather than training separate binary classifiers per category or using rule-based heuristics. Inherits Llama Guard 3's taxonomy design but extends it with visual understanding.

vs others: Provides granular per-category scores in one API call, enabling policy-based routing, whereas binary classifiers (safe/unsafe) require downstream logic to determine which violation type occurred, and rule-based systems are brittle to paraphrasing.

via “prompt-categorization-and-tagging”

Search prompts from top prompt engineers. Sell your own prompts.

via “use-case-categorized-prompt-discovery”

Unique: Uses intent-based categorization (productivity, education, chatbots) rather than technique-based taxonomy (few-shot, chain-of-thought, role-play), lowering the barrier for non-technical users

vs others: More accessible than PromptBase's technique-focused filtering for beginners, but less granular than community-driven repositories that support user-defined tags and cross-category search

via “category-based prompt filtering and organization”

Unique: Uses simple flat category taxonomy with user-assigned tags rather than hierarchical or algorithmic categorization, enabling rapid contributor onboarding but accepting lower discoverability precision

vs others: Simpler to implement and maintain than hierarchical taxonomies or ML-based categorization, but provides less precise filtering and requires users to know which category to browse

via “prompt categorization by use case and domain”

Unique: Implements a 70-category taxonomy specifically designed for generative AI use cases (creative, business, technical domains) rather than generic content categories. This domain-specific categorization enables more precise discovery than generic taxonomies used by content platforms.

vs others: More granular and domain-specific than generic search engines, but less flexible than full-text search or semantic search for discovering cross-domain prompts.

via “prompt-categorization-and-tagging”