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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Classifies incoming prompts across multiple harm categories (e.g., violence, illegal activity, sexual content, hate speech, self-harm) using a fine-tuned language model trained on diverse adversarial examples. The model learns to recognize harmful intent patterns across different risk domains simultaneously, enabling nuanced detection beyond binary safe/unsafe classification. Outputs confidence scores per harm category to support downstream risk-based routing decisions.
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 alternatives: 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
Analyzes LLM-generated responses to identify harmful content that slipped past prompt filtering, classifying violations across the same harm taxonomy as prompt detection. Uses a separate classification head trained on model outputs paired with human safety judgments, enabling detection of harmful content generation even when the initial prompt appeared benign. Supports both full-response analysis and streaming token-level detection for real-time filtering.
Unique: Specifically trained on LLM-generated text rather than generic harmful content, using a dataset of model outputs paired with human safety judgments — captures model-specific failure modes (e.g., verbose harmful explanations) that generic classifiers miss
vs alternatives: More effective than post-hoc content filters (like regex or keyword matching) because it understands semantic intent and can detect harmful content expressed in novel ways; more targeted than general toxicity classifiers because it's calibrated for LLM output patterns
Identifies when an LLM refuses to answer a prompt and classifies the refusal reason (safety concern, capability limitation, policy violation, etc.) using a specialized classifier trained on refusal patterns. This enables distinguishing between legitimate refusals (model correctly declining harmful requests) and false refusals (model unnecessarily blocking benign requests), supporting both safety auditing and user experience optimization. Outputs refusal confidence and category to enable downstream handling (e.g., rephrasing suggestions, escalation).
Unique: Treats refusal detection as a distinct classification task rather than a binary safe/unsafe decision, enabling fine-grained analysis of model behavior — captures the nuance that some refusals are appropriate (blocking harmful requests) while others are false positives (blocking benign requests)
vs alternatives: More sophisticated than simple keyword matching for refusal detection because it understands semantic refusal patterns; enables safety auditing that generic classifiers cannot support by categorizing refusal reasons
Provides a structured dataset of 10K+ adversarial prompts spanning 13 harm categories, each annotated by human raters for prompt harmfulness, response harmfulness, and refusal appropriateness. The dataset includes diverse attack patterns (jailbreaks, prompt injections, social engineering) and edge cases, enabling researchers and builders to train, evaluate, and benchmark safety classifiers. Supports both supervised fine-tuning of safety models and evaluation of existing LLM safety mechanisms.
Unique: Combines three annotation dimensions (prompt harmfulness, response harmfulness, refusal appropriateness) in a single dataset, enabling multi-task learning and comprehensive safety evaluation — most public datasets focus on only one dimension
vs alternatives: More comprehensive than generic toxicity datasets (e.g., Jigsaw) because it's specifically curated for adversarial prompts and LLM jailbreaks; more detailed than simple safe/unsafe labels because it provides fine-grained harm categories and multi-dimensional annotations
Provides a fine-tuned language model (based on Llama 2 or similar backbone) trained via multi-task learning to simultaneously predict prompt harmfulness, response harmfulness, and refusal appropriateness. The model uses shared representations for all three tasks, enabling efficient inference and transfer learning across safety dimensions. Available in multiple sizes (7B, 13B parameters) to support different latency/accuracy trade-offs in production deployments.
Unique: Uses multi-task learning with shared representations across three safety dimensions (prompt harm, response harm, refusal appropriateness) rather than separate single-task models, reducing model size and inference latency while improving generalization through task-specific regularization
vs alternatives: More efficient than running three separate safety classifiers because it shares parameters and inference compute; more accurate than single-task models on individual tasks due to regularization from auxiliary tasks; more flexible than API-based safety services because it runs locally without network latency or data transmission concerns
Defines a structured taxonomy of 13 harm categories (violence, illegal activity, sexual content, hate speech, self-harm, etc.) with clear definitions and annotation guidelines for consistent human labeling. The schema supports multi-label annotation (a single prompt can belong to multiple categories) and confidence scoring, enabling nuanced safety classification beyond binary safe/unsafe decisions. Includes inter-rater agreement metrics and quality control procedures for maintaining annotation consistency.
Unique: Provides a comprehensive 13-category taxonomy specifically designed for LLM safety rather than generic content moderation, with multi-label support enabling fine-grained classification of prompts that span multiple harm dimensions
vs alternatives: More detailed than OpenAI's moderation API categories (which uses ~6 categories) and more LLM-specific than general content moderation taxonomies; enables richer safety analysis and more targeted mitigation strategies
Provides standardized evaluation metrics and benchmark results for safety classifiers, including precision, recall, F1-score, and ROC-AUC across all 13 harm categories. Enables comparison of different safety approaches (API-based, fine-tuned models, rule-based systems) on a common test set with consistent evaluation methodology. Includes ablation studies showing the contribution of different training techniques (multi-task learning, data augmentation, etc.) to overall performance.
Unique: Provides multi-dimensional evaluation across 13 harm categories with per-category metrics rather than a single aggregate score, enabling fine-grained analysis of safety classifier performance and identification of specific weaknesses
vs alternatives: More comprehensive than simple accuracy metrics because it includes precision, recall, and ROC-AUC; more actionable than generic benchmarks because it's specific to safety classification and includes category-level breakdowns
Provides training scripts, loss functions, and hyperparameter configurations for fine-tuning the WildGuard base model on domain-specific safety concerns with minimal labeled data. Implements techniques like low-rank adaptation (LoRA), data augmentation, and curriculum learning to improve sample efficiency and reduce overfitting. Includes evaluation utilities for monitoring validation performance and early stopping to prevent degradation on the original safety tasks.
Unique: Provides end-to-end fine-tuning infrastructure with parameter-efficient techniques (LoRA) and multi-task regularization to prevent catastrophic forgetting, enabling safe domain adaptation without requiring full model retraining or massive labeled datasets
vs alternatives: More efficient than fine-tuning from scratch because it leverages pre-trained representations; more practical than API-based safety services because it enables customization without vendor lock-in; more accessible than building custom classifiers from scratch because it provides templates and best practices
+1 more capabilities
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs WildGuard at 59/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
+5 more capabilities