{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-promptbench","slug":"pypi-promptbench","name":"promptbench","type":"benchmark","url":"https://github.com/microsoft/promptbench","page_url":"https://unfragile.ai/pypi-promptbench","categories":["testing-quality"],"tags":["pytorch","large","language","models","prompt","tuning","dyval","evaluation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-promptbench__cap_0","uri":"capability://tool.use.integration.unified.multi.model.interface.with.factory.pattern","name":"unified-multi-model-interface-with-factory-pattern","description":"Provides a factory-pattern-based abstraction layer (LLMModel and VLMModel classes) that unifies access to heterogeneous language and vision-language models across multiple providers (OpenAI, Anthropic, local models, etc.). The system abstracts API differences, authentication, and request/response formatting so users interact with a consistent interface regardless of underlying model implementation, reducing boilerplate and enabling model swapping without code changes.","intents":["I want to benchmark multiple LLM providers without rewriting code for each API","I need to switch between cloud-hosted and locally-deployed models in my evaluation pipeline","I want to add a new model provider to my benchmark suite without refactoring existing evaluation code"],"best_for":["ML researchers comparing model performance across multiple providers","teams building model evaluation frameworks that need provider agnosticism","developers prototyping multi-model applications before committing to a single provider"],"limitations":["Factory pattern adds indirection layer — debugging model-specific issues requires understanding both the abstraction and concrete implementation","Not all model capabilities are exposed through the unified interface — provider-specific features may require direct API calls","Latency overhead from abstraction layer is negligible but request batching optimizations may be lost"],"requires":["Python 3.8+","PyTorch (for tensor operations and model loading)","API keys for cloud providers (OpenAI, Anthropic, etc.) or local model weights"],"input_types":["text prompts","structured model configuration objects"],"output_types":["text completions","structured model responses with metadata"],"categories":["tool-use-integration","model-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_1","uri":"capability://safety.moderation.adversarial.prompt.attack.simulation.multi.level","name":"adversarial-prompt-attack-simulation-multi-level","description":"Implements a multi-level adversarial attack framework that generates adversarial prompt variations at character, word, sentence, and semantic levels (DeepWordBug, TextBugger, TextFooler, BertAttack, CheckList, StressTest, human-crafted attacks). Each attack method applies different perturbation strategies to test model robustness — character-level attacks corrupt individual characters, word-level attacks substitute semantically similar words, sentence-level attacks modify sentence structure, and semantic-level attacks alter meaning while preserving surface form.","intents":["I want to evaluate how robust my LLM is against adversarial prompt variations","I need to test if my model maintains performance when prompts contain typos, word substitutions, or paraphrases","I want to identify vulnerabilities in my model's prompt handling before deploying to production"],"best_for":["security researchers evaluating LLM robustness and adversarial resilience","teams building production LLM systems that need adversarial testing","researchers studying prompt injection and jailbreak vulnerabilities"],"limitations":["Character and word-level attacks may not preserve semantic meaning — results may not reflect real-world adversarial scenarios","Attack success depends on model's tokenization and vocabulary — attacks optimized for one model may not transfer to another","Computational cost scales with dataset size and number of attack methods — evaluating large datasets with all attack types can be expensive","No built-in defense mechanisms — framework is for evaluation only, not mitigation"],"requires":["Python 3.8+","PyTorch","BERT or similar NLP model for word-level attacks (TextFooler, BertAttack)","Target LLM to attack (via unified model interface)"],"input_types":["text prompts","attack configuration (attack type, perturbation rate)"],"output_types":["adversarial prompt variants","model responses to adversarial inputs","attack success metrics (e.g., success rate, semantic similarity)"],"categories":["safety-moderation","adversarial-testing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_10","uri":"capability://tool.use.integration.extensible.framework.for.custom.models.datasets.attacks","name":"extensible-framework-for-custom-models-datasets-attacks","description":"Provides extension points and documentation for adding custom models, datasets, prompt engineering techniques, and adversarial attacks to the framework. The system uses abstract base classes and registration mechanisms that allow users to implement custom components that integrate seamlessly with the existing evaluation pipeline. 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Each task type (arithmetic, logic, deduction, reachability) has a generator that creates valid test instances with known ground truth.","intents":["I want to evaluate my model on reasoning tasks without worrying about test set contamination from training data","I need to test model performance across varying difficulty levels on the same task type","I want to generate unlimited evaluation samples to stress-test my model's reasoning capabilities"],"best_for":["researchers studying LLM reasoning and generalization beyond memorization","teams evaluating models on reasoning-heavy tasks (math, logic, planning)","developers building robust evaluation suites that can't be gamed by training on test data"],"limitations":["Generated tasks may not reflect real-world complexity distributions — synthetic generation can miss edge cases present in natural data","Evaluation is limited to task types with formal specifications (arithmetic, logic, deduction, reachability) — cannot generate arbitrary reasoning tasks","Computational cost of on-the-fly generation adds latency compared to pre-computed test sets","Ground truth generation assumes well-defined task semantics — ambiguous or open-ended tasks cannot be evaluated this way"],"requires":["Python 3.8+","PyTorch","Target LLM for evaluation","Task specification (task type, difficulty parameters)"],"input_types":["task type (arithmetic, boolean_logic, deduction, reachability)","difficulty parameters (number of steps, operand range, graph size, etc.)","number of samples to generate"],"output_types":["generated test instances with ground truth","model predictions on generated instances","accuracy metrics by difficulty level"],"categories":["data-processing-analysis","evaluation-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_3","uri":"capability://data.processing.analysis.efficient.multi.prompt.evaluation.with.performance.prediction","name":"efficient-multi-prompt-evaluation-with-performance-prediction","description":"Implements PromptEval, an efficient evaluation method that predicts model performance on large datasets using performance data from a small sample. The system trains a lightweight predictor on a small subset of prompts and their corresponding model outputs, then extrapolates to estimate performance across the full dataset without evaluating every prompt. This reduces computational cost by orders of magnitude while maintaining reasonable accuracy estimates.","intents":["I want to evaluate multiple prompt variations without running inference on every prompt against the full dataset","I need to quickly estimate which prompts will perform best before committing to full evaluation","I want to reduce the computational cost of multi-prompt evaluation by 10-100x"],"best_for":["researchers doing prompt engineering and need to evaluate many prompt variants quickly","teams with limited computational budgets who need to evaluate multiple models/prompts","developers building prompt optimization pipelines that need fast feedback loops"],"limitations":["Prediction accuracy depends on sample representativeness — biased samples lead to poor extrapolation","Assumes performance distribution is smooth and predictable — fails on datasets with high variance or multimodal distributions","Requires training a predictor model — adds complexity and potential for overfitting on small samples","Predictions are estimates, not ground truth — unsuitable for final model selection without validation on full dataset"],"requires":["Python 3.8+","PyTorch","Target LLM for evaluation","Dataset with at least 100+ samples for reasonable prediction","Small sample of prompts to evaluate (typically 10-20% of full dataset)"],"input_types":["prompt variants","dataset samples","model outputs on sample","performance metrics"],"output_types":["predicted performance on full dataset","confidence intervals for predictions","ranking of prompt variants by predicted performance"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_4","uri":"capability://text.generation.language.prompt.engineering.technique.library.with.chain.of.thought","name":"prompt-engineering-technique-library-with-chain-of-thought","description":"Provides a library of prompt engineering methods including Chain-of-Thought (CoT), Emotion Prompt, Expert Prompting, and other advanced techniques that modify prompts to improve model reasoning and performance. Each technique implements a specific prompt transformation strategy — CoT adds step-by-step reasoning instructions, Emotion Prompt injects emotional context, Expert Prompting frames the model as a domain expert. The system applies these transformations to input prompts before sending them to the model.","intents":["I want to improve my model's performance on reasoning tasks by using chain-of-thought prompting","I need to test multiple prompt engineering techniques to find which works best for my task","I want to systematically apply prompt engineering methods to my evaluation dataset"],"best_for":["researchers studying prompt engineering effectiveness across models and tasks","developers optimizing LLM performance without fine-tuning","teams building prompt optimization pipelines that need a library of proven techniques"],"limitations":["Technique effectiveness varies significantly by model and task — no single technique works universally","Some techniques (CoT) increase token consumption and latency by requiring longer outputs","Techniques are heuristic-based and not theoretically grounded — results may not generalize to new domains","Combining multiple techniques can lead to diminishing returns or interference"],"requires":["Python 3.8+","Target LLM for evaluation","Input prompts or tasks"],"input_types":["text prompts","task descriptions","technique selection (CoT, Emotion, Expert, etc.)"],"output_types":["transformed prompts","model outputs with applied techniques","performance metrics comparing techniques"],"categories":["text-generation-language","prompt-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_5","uri":"capability://data.processing.analysis.dataset.loader.with.multi.format.support","name":"dataset-loader-with-multi-format-support","description":"Implements a DatasetLoader class that manages loading and preprocessing of diverse datasets for both language and multi-modal evaluation (GLUE, MMLU, BIG-Bench Hard, ImageNet, COCO, etc.). The loader abstracts dataset-specific preprocessing, normalization, and format conversion, providing a unified interface to access different datasets. It handles dataset downloading, caching, splitting, and batching automatically.","intents":["I want to load standard benchmarks (GLUE, MMLU) without writing custom data loading code","I need to evaluate my model on multiple datasets with consistent preprocessing","I want to create custom datasets that integrate seamlessly with the evaluation framework"],"best_for":["researchers benchmarking models across multiple standard datasets","teams building evaluation pipelines that need consistent data handling","developers extending PromptBench with new datasets"],"limitations":["Limited to pre-configured datasets — adding new datasets requires implementing a custom loader","Dataset-specific preprocessing may not be optimal for all models — some models may need different normalization","Caching can consume significant disk space for large datasets (ImageNet, COCO)","No built-in data augmentation or synthetic data generation"],"requires":["Python 3.8+","PyTorch","Disk space for dataset caching (varies by dataset, 1GB-100GB+)","Internet connection for initial dataset download"],"input_types":["dataset name (GLUE, MMLU, ImageNet, etc.)","dataset configuration (split, subset, preprocessing options)"],"output_types":["loaded dataset with samples","batched data loaders","dataset metadata (size, splits, task type)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_6","uri":"capability://image.visual.vision.language.model.evaluation.interface","name":"vision-language-model-evaluation-interface","description":"Provides a VLMModel class that extends the unified model interface to support Vision-Language Models (VLMs) that process both text and image inputs. The interface handles multi-modal input encoding, image preprocessing (resizing, normalization), and multi-modal output generation. It abstracts differences between VLM architectures (CLIP, BLIP, LLaVA, etc.) to provide consistent evaluation across vision-language tasks.","intents":["I want to benchmark vision-language models on image captioning and visual question answering tasks","I need to evaluate VLMs using the same evaluation framework as my LLM benchmarks","I want to test adversarial attacks on vision-language models (image perturbations + prompt attacks)"],"best_for":["researchers evaluating vision-language models across multiple benchmarks","teams building multi-modal evaluation pipelines","developers studying robustness of VLMs to adversarial inputs"],"limitations":["Image preprocessing is standardized but may not be optimal for all VLM architectures","No built-in image augmentation or adversarial image perturbations — only prompt-level attacks","Evaluation is limited to VLMs that accept text + image inputs — cannot evaluate image-only or text-only models","Computational cost is higher than text-only evaluation due to image encoding"],"requires":["Python 3.8+","PyTorch with CUDA support (recommended for image processing)","Vision-language model weights or API access","Image datasets (COCO, Flickr, etc.)"],"input_types":["text prompts","images (PNG, JPEG, etc.)","multi-modal task specifications"],"output_types":["text responses (captions, answers)","confidence scores","multi-modal evaluation metrics"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_7","uri":"capability://data.processing.analysis.evaluation.metrics.computation.with.task.specific.scoring","name":"evaluation-metrics-computation-with-task-specific-scoring","description":"Implements an evaluation system (eval.py) that computes task-specific metrics for different benchmark types. The system supports classification metrics (accuracy, F1, precision, recall), generation metrics (BLEU, ROUGE, METEOR), and reasoning metrics (exact match, semantic similarity). Each metric is implemented with proper handling of edge cases, and the system can aggregate metrics across datasets and prompt variations.","intents":["I want to compute standard evaluation metrics (accuracy, F1, BLEU) for my model outputs","I need to aggregate metrics across multiple datasets and prompt variations","I want to compare model performance using multiple metrics simultaneously"],"best_for":["researchers evaluating model performance using standard metrics","teams building evaluation pipelines that need consistent metric computation","developers comparing models across multiple benchmarks"],"limitations":["Metrics are task-specific — using wrong metric for task type produces meaningless results","Some metrics (BLEU, ROUGE) have known limitations for evaluating neural model outputs","No built-in statistical significance testing — results may not be statistically meaningful","Metric computation assumes well-formatted outputs — malformed outputs may cause errors"],"requires":["Python 3.8+","PyTorch","Model outputs and ground truth labels","Metric-specific dependencies (NLTK for BLEU/ROUGE, etc.)"],"input_types":["model predictions (text, logits, or structured outputs)","ground truth labels","metric type (accuracy, F1, BLEU, ROUGE, etc.)"],"output_types":["scalar metric values","per-sample metric scores","aggregated metrics across dataset"],"categories":["data-processing-analysis","evaluation-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptbench__cap_8","uri":"capability://planning.reasoning.meta.probing.agents.for.model.capability.analysis","name":"meta-probing-agents-for-model-capability-analysis","description":"Implements Meta Probing Agents (MPA), a system for systematically analyzing model capabilities through targeted probing tasks. The MPA framework generates probing tasks that test specific linguistic or reasoning capabilities (syntax, semantics, reasoning, knowledge), then analyzes model performance to identify capability gaps. 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The system creates visualizations of metric distributions, performance comparisons across models/prompts, adversarial attack success rates, and capability analysis results. It supports exporting results in multiple formats (CSV, JSON, plots) for further analysis and reporting.","intents":["I want to visualize how my model performs across different prompt variations","I need to create comparison plots showing performance differences between models","I want to generate reports showing adversarial attack success rates and robustness metrics"],"best_for":["researchers presenting evaluation results in papers and presentations","teams analyzing evaluation results to identify trends and patterns","developers creating dashboards for model monitoring and comparison"],"limitations":["Visualizations are static — no interactive exploration of results","Limited customization of plots — may not match specific publication requirements","Requires matplotlib/seaborn dependencies — adds to package size","No built-in statistical analysis — visualization is descriptive only"],"requires":["Python 3.8+","matplotlib, seaborn, or similar visualization libraries","Evaluation results in standard format"],"input_types":["evaluation metrics (scalars, arrays)","model/prompt/dataset names","visualization configuration (plot type, colors, labels)"],"output_types":["PNG/PDF plots","CSV/JSON result tables","HTML reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch (for tensor operations and model loading)","API keys for cloud providers (OpenAI, Anthropic, etc.) or local model weights","PyTorch","BERT or similar NLP model for word-level attacks (TextFooler, BertAttack)","Target LLM to attack (via unified model interface)","Understanding of PromptBench architecture (Model System, Dataset System, etc.)","Knowledge of abstract base classes and inheritance patterns","Target LLM for evaluation","Task specification (task type, difficulty parameters)"],"failure_modes":["Factory pattern adds indirection layer — debugging model-specific issues requires understanding both the abstraction and concrete implementation","Not all model capabilities are exposed through the unified interface — provider-specific features may require direct API calls","Latency overhead from abstraction layer is negligible but request batching optimizations may be lost","Character and word-level attacks may not preserve semantic meaning — results may not reflect real-world adversarial scenarios","Attack success depends on model's tokenization and vocabulary — attacks optimized for one model may not transfer to another","Computational cost scales with dataset size and number of attack methods — evaluating large datasets with all attack types can be expensive","No built-in defense mechanisms — framework is for evaluation only, not mitigation","Extension points require understanding the framework architecture — steep learning curve for new contributors","No formal extension API documentation — requires reading source code to understand patterns","Breaking changes in core framework can break custom extensions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:25.058Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-promptbench","compare_url":"https://unfragile.ai/compare?artifact=pypi-promptbench"}},"signature":"htaaPuuqAV6oa6BL+8eEr93OtukOlXBoPF26nfINMvFaDoruqsPlRXeHXM6uRtaEEgUWPrmSb31Lj3h+W+QqDw==","signedAt":"2026-06-21T21:29:23.621Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-promptbench","artifact":"https://unfragile.ai/pypi-promptbench","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-promptbench","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}