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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates adversarial toxic text examples using the ALICE (Adversarial Language-model Interaction for Classifier Evasion) framework, which implements a beam search algorithm that combines GPT-3 language model probabilities with toxicity classifier confidence scores to produce fluent text that evades existing hate speech detection systems. The framework iteratively refines candidates by weighting both language model likelihood and adversarial objectives, enabling discovery of subtle, implicit hate speech without explicit slurs.
Unique: Implements a dual-objective beam search that jointly optimizes for language model fluency AND classifier evasion, rather than treating adversarial generation as a post-hoc attack. The scoring system weights both GPT-3 log probabilities and classifier confidence, enabling discovery of naturally-fluent adversarial examples that existing classifiers miss.
vs alternatives: More sophisticated than simple prompt-based generation because it uses active feedback from classifiers during generation, producing more realistic adversarial examples than rule-based or gradient-based attacks that may produce unnatural text.
Converts human-written toxic demonstrations into structured few-shot prompts that guide GPT-3 to generate similar toxic content across 13 minority groups. The system uses a configurable prompt template that includes human examples as in-context demonstrations, enabling controlled generation of group-specific toxic statements without requiring manual prompt engineering for each group.
Unique: Uses a systematic, group-agnostic prompt template that enables consistent generation across 13 minority groups from a single set of human demonstrations, rather than requiring group-specific prompt engineering. The demonstrations_to_prompts.py pipeline abstracts away group-specific details, allowing researchers to focus on demonstration quality rather than prompt tuning.
vs alternatives: More scalable than manual prompt engineering because it automatically generates group-specific prompts from a single demonstration set, reducing the effort needed to create balanced datasets across multiple demographic groups.
Provides evaluation metrics for assessing classifier robustness on generated adversarial datasets, including accuracy, precision, recall, F1-score, and adversarial success rate (percentage of generated examples misclassified as benign). The system enables benchmarking of different classifiers on the same adversarial dataset and comparison of robustness across different generation strategies.
Unique: Provides adversarial-specific metrics (adversarial success rate) in addition to standard classification metrics, enabling direct measurement of how well classifiers resist adversarial examples. The system supports per-group evaluation, revealing whether classifiers have disparate robustness across different target groups.
vs alternatives: More comprehensive than standard classification metrics because it includes adversarial-specific measures and per-group analysis, enabling researchers to identify both overall robustness issues and fairness disparities across demographic groups.
Integrates pre-trained hate speech classifiers (HateBERT, RoBERTa) into the generation pipeline to provide real-time toxicity scoring during beam search. The integration abstracts classifier inference behind a unified interface, enabling the ALICE framework to query classifier confidence scores for candidate text and use those scores as feedback signals to guide adversarial generation.
Unique: Provides a unified classifier interface that abstracts away model-specific details (tokenization, inference, output format), enabling the ALICE framework to treat classifiers as interchangeable scoring functions. This design allows researchers to swap classifiers without modifying the core beam search algorithm.
vs alternatives: More flexible than hard-coded classifier integration because it uses a plugin-style architecture that supports multiple classifier backends, enabling researchers to evaluate adversarial robustness across different detection models without rewriting generation code.
Implements a beam search algorithm that maintains multiple candidate text sequences and scores each candidate using a weighted combination of language model probability (fluency) and classifier confidence (adversarial objective). At each decoding step, the algorithm expands candidates by sampling from the language model, scores all expansions, and retains the top-k candidates based on the combined objective, enabling discovery of text that is both fluent and adversarial.
Unique: Combines language model and classifier scores in a single beam search objective, rather than generating text first and then filtering for adversarial properties. This joint optimization during decoding produces more natural adversarial examples because the language model is aware of the adversarial objective throughout generation.
vs alternatives: More efficient than post-hoc adversarial attacks (gradient-based or genetic algorithms) because it integrates adversarial feedback into the generation process itself, avoiding the need to generate and filter large numbers of candidates.
Provides a standardized interface for loading, organizing, and distributing the generated toxic and benign datasets through Hugging Face Hub. The system structures data with consistent annotations (toxicity labels, target groups, generation method), enables easy filtering and splitting for train/test/validation, and supports multiple serialization formats (JSON, CSV, Parquet) for compatibility with different ML frameworks.
Unique: Distributes datasets through Hugging Face Hub with standardized metadata and filtering capabilities, rather than requiring manual download and parsing. The structured format enables researchers to load datasets with a single function call and filter by multiple dimensions (group, toxicity, generation method) without custom code.
vs alternatives: More accessible than raw dataset files because it provides a unified interface through Hugging Face Hub, enabling one-line dataset loading and automatic versioning/caching, compared to manually downloading and parsing CSV/JSON files.
Generates toxic statements that contain no explicit slurs or profanity but express hateful sentiment through subtle language, innuendo, and implicit bias. The system uses human demonstrations and the ALICE framework to discover linguistic patterns that convey toxicity without triggering keyword-based filters, enabling evaluation of classifiers' ability to detect implicit hate speech that relies on context and coded language.
Unique: Focuses specifically on implicit and subtle forms of toxicity rather than explicit slurs, using the ALICE framework to discover linguistic patterns that evade keyword-based filters. The system generates examples that are adversarial to classifiers precisely because they lack obvious toxic markers.
vs alternatives: More challenging than datasets of explicit hate speech because implicit toxicity requires classifiers to understand context and linguistic nuance, making it a more realistic evaluation of real-world content moderation challenges where bad actors use coded language and innuendo.
Generates balanced toxic and benign datasets targeting 13 distinct minority groups (e.g., religious groups, ethnic groups, LGBTQ+ communities) using the same generation pipeline and human demonstrations adapted for each group. The system ensures comparable coverage and toxicity patterns across groups, enabling evaluation of classifier fairness and bias across different demographic targets.
Unique: Systematically generates comparable toxic datasets across 13 minority groups using a unified pipeline, rather than creating separate datasets for each group. This enables direct comparison of toxicity patterns and classifier performance across groups, making fairness evaluation straightforward.
vs alternatives: More comprehensive than single-group datasets because it enables fairness analysis across multiple demographic targets, allowing researchers to identify whether classifiers have disparate performance or bias against specific groups.
+3 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 ToxiGen at 60/100.
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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