{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"trustllm","slug":"trustllm","name":"TrustLLM","type":"benchmark","url":"https://github.com/HowieHwong/TrustLLM","page_url":"https://unfragile.ai/trustllm","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"trustllm__cap_0","uri":"capability://safety.moderation.multi.dimensional.trustworthiness.evaluation.across.6.core.dimensions","name":"multi-dimensional trustworthiness evaluation across 6 core dimensions","description":"Orchestrates systematic evaluation of LLM outputs across Truthfulness, Safety, Fairness, Robustness, Privacy, and Machine Ethics using a modular evaluation pipeline. Each dimension contains 2-4 sub-tasks with dedicated evaluation logic (pattern matching, model-based grading, deterministic metrics). The framework loads 30+ datasets, routes them through dimension-specific evaluators, and aggregates results into comparative rankings across models.","intents":["Compare trustworthiness profiles of multiple LLMs across safety, fairness, and ethics dimensions","Identify which trustworthiness dimensions a model fails on before production deployment","Benchmark custom fine-tuned models against public LLMs using standardized evaluation criteria","Track trustworthiness regressions across model versions and training iterations"],"best_for":["AI safety researchers evaluating model reliability","Enterprise teams vetting LLMs for regulated industries (finance, healthcare, legal)","Model developers building trustworthy AI systems","Compliance officers documenting LLM safety assessments"],"limitations":["Evaluation latency scales with dataset size and model count — 30+ datasets × N models can require hours to days","Model-based evaluators (GPT-4) introduce cost and potential bias from the evaluator model itself","Some dimensions (e.g., Privacy Leakage) rely on heuristics rather than ground truth, limiting precision","No real-time streaming evaluation — requires batch processing of all responses before evaluation begins"],"requires":["Python 3.8+","API keys for target models (OpenAI, Anthropic, Google, etc.) or local HuggingFace model weights","API keys for evaluators (OpenAI GPT-4 for AutoEvaluator, Perspective API for toxicity)","30+ GB disk space for benchmark datasets","Internet connectivity for online model APIs or local GPU for inference"],"input_types":["Benchmark datasets (JSON format with prompts, expected outputs, metadata)","Model configuration (model name, API endpoint, temperature, max_tokens)","Evaluation prompts (dimension-specific templates for grading)"],"output_types":["Evaluation scores per dimension (0-100 scale or binary pass/fail)","Detailed reports with per-sample explanations and error analysis","Comparative rankings and leaderboards across models","JSON result files with raw scores and aggregated metrics"],"categories":["safety-moderation","data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_1","uri":"capability://automation.workflow.two.stage.generation.then.evaluation.pipeline.orchestration","name":"two-stage generation-then-evaluation pipeline orchestration","description":"Implements a decoupled workflow where Stage 1 (LLMGeneration) runs inference on all benchmark prompts and caches responses to JSON, then Stage 2 (evaluation functions) processes cached outputs without re-querying models. Generation stage uses multi-threaded API calls (default GROUP_SIZE=8) for online models or fastchat backend for local models. Evaluation stage applies dimension-specific logic (regex, model-based grading, API calls) to pre-generated responses, enabling cost-efficient re-evaluation and result reproducibility.","intents":["Run expensive model inference once and evaluate multiple times with different metrics without re-querying","Parallelize API calls to multiple online models to reduce total benchmark runtime","Reproduce evaluation results deterministically by storing and replaying cached model outputs","Swap evaluation strategies (e.g., GPT-4 grader → Longformer classifier) without re-running inference"],"best_for":["Teams benchmarking 5+ models where re-inference is cost-prohibitive","Researchers iterating on evaluation metrics and wanting reproducible baselines","Developers integrating TrustLLM into CI/CD pipelines for automated model testing","Organizations with limited API budgets needing to minimize redundant inference calls"],"limitations":["Cached responses become stale if model weights or API behavior changes — no automatic invalidation","Multi-threading (GROUP_SIZE=8) may hit rate limits on some APIs; requires manual tuning per provider","Evaluation results depend on cached responses — cannot re-sample or adjust temperature without re-running generation","No incremental evaluation — must re-evaluate all samples even if only one metric changes"],"requires":["Disk space for JSON response cache (typically 100MB-1GB per model depending on dataset size)","Network connectivity for Stage 1 (generation); Stage 2 (evaluation) can run offline if using local evaluators","Python 3.8+ with trustllm package installed","Model API credentials for Stage 1; optional for Stage 2 if using pattern-matching evaluators"],"input_types":["Benchmark dataset files (JSON with prompts, metadata, expected outputs)","Model configuration (model ID, API endpoint, inference parameters)","Cached response JSON files (output of Stage 1, input to Stage 2)"],"output_types":["Stage 1: JSON files with model responses, timestamps, and metadata","Stage 2: Evaluation scores, per-sample explanations, aggregated metrics, leaderboard rankings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_10","uri":"capability://safety.moderation.longformer.based.toxicity.classification.for.safety.evaluation","name":"longformer-based toxicity classification for safety evaluation","description":"Implements HuggingFaceEvaluator class that uses a pre-trained Longformer classifier (fine-tuned on toxicity detection) to score model responses for offensive language and harmful content. Loads model weights from HuggingFace, batches inputs for efficiency, and outputs toxicity scores (0-1 scale). Runs locally without API calls, enabling fast and cost-free toxicity evaluation. Complements Perspective API for redundant toxicity scoring.","intents":["Score model responses for toxicity and offensive language without API calls","Evaluate toxicity at scale with low latency and zero API cost","Provide local alternative to Perspective API for privacy-sensitive deployments","Detect toxic outputs in real-time or batch evaluation"],"best_for":["Teams with privacy constraints avoiding external toxicity APIs","Organizations evaluating toxicity at scale without API budgets","Researchers studying toxicity detection in language models","Developers building content moderation systems"],"limitations":["Longformer classifier is trained on specific toxicity datasets; may not generalize to all toxic content","Requires GPU for efficient inference; CPU inference is slow for large batches","Model weights (~500MB) must be downloaded and cached locally","Toxicity definition is dataset-dependent; may have cultural or linguistic biases"],"requires":["GPU with sufficient VRAM (4GB+ recommended for efficient batching)","Python 3.8+ with trustllm, transformers, and torch installed","Internet connectivity to download Longformer weights from HuggingFace (one-time)","500MB disk space for model weights"],"input_types":["Model responses (text)","Batch size configuration (for efficiency tuning)"],"output_types":["Toxicity scores (0-1 scale per response)","Batch aggregation (mean toxicity, distribution)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_11","uri":"capability://safety.moderation.perspective.api.integration.for.external.toxicity.scoring","name":"perspective api integration for external toxicity scoring","description":"Integrates Google's Perspective API to score model responses for toxicity, severe toxicity, profanity, and other harmful attributes. Sends responses to Perspective API, parses structured toxicity scores, and aggregates results. Provides ground-truth toxicity scoring from an external, widely-used service. Complements local Longformer classifier for redundant toxicity evaluation and cross-validation.","intents":["Score model responses using industry-standard toxicity detection (Perspective API)","Cross-validate local toxicity classifiers against external ground truth","Measure multiple toxicity dimensions (toxicity, severe toxicity, profanity, etc.)","Benchmark toxicity against Perspective API's toxicity standards"],"best_for":["Teams requiring industry-standard toxicity scoring","Organizations validating local toxicity classifiers against external benchmarks","Researchers studying toxicity detection accuracy","Compliance teams documenting toxicity assessment with third-party validation"],"limitations":["Perspective API has rate limits (~1 request/second); large-scale evaluation requires batching and delays","API cost is free but requires quota approval from Google","Toxicity scores are opaque; no visibility into how Perspective API computes scores","API may refuse requests for certain content types; error handling required"],"requires":["API key for Google Perspective API (free, requires quota approval)","Python 3.8+ with trustllm and google-api-python-client installed","Network connectivity to Perspective API"],"input_types":["Model responses (text, up to 20K characters per request)"],"output_types":["Toxicity scores (0-1 scale for multiple attributes: toxicity, severe toxicity, profanity, etc.)","Aggregated metrics (mean toxicity, distribution)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_12","uri":"capability://data.processing.analysis.multi.model.comparative.ranking.and.leaderboard.generation","name":"multi-model comparative ranking and leaderboard generation","description":"Aggregates evaluation scores across all models and dimensions to generate comparative rankings and leaderboards. Computes per-dimension scores, overall trustworthiness score (weighted average), and model rankings. Generates visualizations (rank cards, score distributions) and exportable leaderboard data (JSON, CSV). Enables fair comparison across heterogeneous models (proprietary, open-source, fine-tuned) evaluated on identical benchmarks.","intents":["Compare trustworthiness profiles of multiple models in a single leaderboard","Identify which models excel in specific dimensions (e.g., high safety but low fairness)","Track model performance over time as new versions are released","Publish transparent model comparisons for stakeholder communication"],"best_for":["Organizations comparing 3+ models for deployment decisions","Researchers publishing model benchmarks and leaderboards","Teams tracking model performance improvements across versions","Stakeholders (executives, compliance) requiring transparent model comparisons"],"limitations":["Rankings are benchmark-specific; models may rank differently on other benchmarks","Weighting of dimensions is arbitrary; different weights produce different rankings","Leaderboards are static snapshots; require re-evaluation to reflect model updates","No statistical significance testing; cannot determine if ranking differences are meaningful"],"requires":["Evaluation results for all models (JSON files from evaluation pipeline)","Python 3.8+ with trustllm, pandas, and matplotlib installed","Dimension weights (configurable, defaults provided)"],"input_types":["Evaluation results (JSON files with per-dimension scores)","Model metadata (model name, provider, version)"],"output_types":["Leaderboard data (JSON, CSV with rankings and scores)","Visualizations (rank cards, score distributions, heatmaps)","Summary statistics (mean scores, score ranges)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_13","uri":"capability://data.processing.analysis.dataset.management.and.benchmark.curation.with.30.integrated.datasets","name":"dataset management and benchmark curation with 30+ integrated datasets","description":"Manages a curated collection of 30+ benchmark datasets across 6 trustworthiness dimensions, with standardized loading, preprocessing, and metadata. Datasets are stored in JSON format with prompts, expected outputs, metadata (difficulty, domain, language), and evaluation instructions. Provides utilities for dataset filtering (by dimension, domain, language), splitting (train/test), and versioning. Enables reproducible benchmarking by pinning dataset versions.","intents":["Access standardized benchmark datasets without manual curation","Filter datasets by dimension, domain, or language for targeted evaluation","Reproduce benchmarks using pinned dataset versions","Extend benchmarks with custom datasets while maintaining compatibility"],"best_for":["Researchers benchmarking models without building custom datasets","Teams standardizing on TrustLLM datasets for reproducible comparisons","Organizations extending TrustLLM with domain-specific datasets","Developers integrating TrustLLM into evaluation pipelines"],"limitations":["Datasets are fixed; no automatic updates when new trustworthiness issues emerge","Dataset coverage is uneven across dimensions and languages (English-heavy)","Some datasets may have quality issues or outdated ground truth","Custom dataset integration requires manual validation and format compliance"],"requires":["TrustLLM package with datasets (30+ GB total)","Python 3.8+ with trustllm installed","Internet connectivity to download datasets (one-time)"],"input_types":["Dataset queries (dimension, domain, language filters)","Custom dataset files (JSON format)"],"output_types":["Loaded datasets (Python objects with prompts, metadata, expected outputs)","Dataset statistics (size, domain distribution, language distribution)","Filtered/split datasets for evaluation"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_14","uri":"capability://automation.workflow.configuration.driven.model.and.evaluator.routing","name":"configuration-driven model and evaluator routing","description":"Centralizes model and evaluator configuration in trustllm/config.py and trustllm/prompt/model_info.json, enabling dynamic routing without code changes. Configuration specifies model provider, API endpoint, credentials, inference parameters (temperature, max_tokens), and evaluator selection (GPT-4, Longformer, Perspective API). Supports environment variable overrides for credential management and multi-environment deployment (dev, staging, prod).","intents":["Switch between models or evaluators by changing configuration files","Manage API credentials securely via environment variables","Deploy TrustLLM across multiple environments with different model/evaluator selections","Enable non-technical users to configure benchmarks without code changes"],"best_for":["Teams managing multiple models and evaluators","Organizations deploying TrustLLM in multiple environments","Developers building configuration-driven evaluation pipelines","Non-technical users configuring benchmarks"],"limitations":["Configuration is static; no dynamic model selection based on runtime conditions","Credential management via environment variables is less secure than secret management systems","Configuration validation is minimal; invalid configs may cause runtime errors","No configuration versioning or rollback; changes are immediate"],"requires":["Configuration files (trustllm/config.py, model_info.json)","Environment variables for credentials (OPENAI_API_KEY, PERSPECTIVE_API_KEY, etc.)","Python 3.8+ with trustllm installed"],"input_types":["Configuration files (JSON, Python)","Environment variables"],"output_types":["Resolved configuration (model provider, evaluator selection, credentials)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_2","uri":"capability://tool.use.integration.unified.model.backend.abstraction.for.online.and.local.inference","name":"unified model backend abstraction for online and local inference","description":"Provides a single LLMGeneration interface that routes to either online APIs (OpenAI, Anthropic, Google, Replicate, DeepInfra, Ernie) or local models (HuggingFace weights via fastchat backend). Configuration-driven model selection via trustllm/config.py and trustllm/prompt/model_info.json allows swapping backends without code changes. Handles API credential management, request formatting, response parsing, and error handling uniformly across heterogeneous model providers.","intents":["Benchmark proprietary models (GPT-4, Claude) and open-source models (Llama, Mistral) in the same evaluation run","Switch between cloud APIs and local inference without modifying benchmark code","Test custom fine-tuned models deployed locally without exposing them to external APIs","Compare cost-per-inference across providers by routing the same prompts to different backends"],"best_for":["Researchers comparing proprietary and open-source models fairly","Teams with privacy constraints requiring local model inference","Organizations evaluating cost-benefit of cloud APIs vs self-hosted models","Developers building multi-model evaluation pipelines"],"limitations":["API credential management is manual — requires setting environment variables or config files for each provider","Response format normalization is provider-specific; some APIs return structured data while others return plain text","Local model inference requires GPU memory proportional to model size; no automatic fallback to quantized versions","Rate limiting and quota management are provider-specific; no built-in adaptive backoff or quota tracking"],"requires":["API keys for online providers (OpenAI, Anthropic, Google, etc.) stored in environment or config","For local models: HuggingFace model weights, fastchat backend, GPU with sufficient VRAM (8GB+ for 7B models)","Python 3.8+ with trustllm and provider-specific SDKs installed","Network connectivity for online models; local models require no external connectivity"],"input_types":["Model configuration (provider name, model ID, API endpoint, inference parameters)","Prompts and benchmark datasets","API credentials (keys, tokens, endpoints)"],"output_types":["Normalized model responses (text, tokens, logits where available)","Metadata (latency, token count, cost estimate, provider)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_3","uri":"capability://safety.moderation.truthfulness.evaluation.with.misinformation.hallucination.and.sycophancy.detection","name":"truthfulness evaluation with misinformation, hallucination, and sycophancy detection","description":"Evaluates model outputs for factual accuracy across 4 sub-tasks: Internal Misinformation (contradictions within responses), External Misinformation (factual errors vs ground truth), Hallucination (fabricated information), and Sycophancy (agreement bias). Uses pattern matching for multiple-choice tasks, GPT-4 auto-evaluation for open-ended responses, and deterministic metrics (exact match, F1 score) for structured outputs. Compares model responses against curated ground truth datasets to quantify factuality gaps.","intents":["Measure how often a model generates false or contradictory information","Detect if a model tends to agree with user prompts regardless of correctness (sycophancy)","Identify hallucination patterns (e.g., fabricated citations, invented facts)","Benchmark factuality improvements across model versions or fine-tuning iterations"],"best_for":["Teams deploying LLMs in fact-sensitive domains (news, research, legal)","Researchers studying hallucination and factuality in language models","QA teams validating model outputs before production","Compliance teams documenting factuality assessments for regulated industries"],"limitations":["Ground truth datasets may be incomplete or outdated; evaluation is only as good as the reference data","GPT-4 auto-evaluation introduces evaluator bias — GPT-4 may favor responses similar to its own style","Sycophancy detection relies on adversarial prompts; may not capture all forms of agreement bias","External misinformation detection requires comprehensive knowledge bases; coverage varies by domain"],"requires":["Truthfulness benchmark datasets (included in TrustLLM package)","API key for OpenAI GPT-4 (for auto-evaluation of open-ended responses)","Ground truth data or reference knowledge base for external misinformation detection","Python 3.8+ with trustllm installed"],"input_types":["Model responses (text)","Prompts and reference answers","Ground truth datasets (JSON with questions, correct answers, distractors)"],"output_types":["Truthfulness score (0-100)","Per-sample evaluation (correct/incorrect, confidence scores)","Breakdown by sub-task (misinformation rate, hallucination rate, sycophancy score)","Detailed explanations for incorrect responses"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_4","uri":"capability://safety.moderation.safety.evaluation.with.jailbreak.toxicity.and.misuse.detection","name":"safety evaluation with jailbreak, toxicity, and misuse detection","description":"Evaluates model safety across 4 sub-tasks: Jailbreak (resistance to adversarial prompts), Toxicity (offensive language detection via Perspective API), Misuse (harmful capability generation), and Exaggerated Safety (false refusals). Uses Longformer classifier for toxicity scoring, pattern matching for refusal-to-answer (RtA) detection, Perspective API for external toxicity scoring, and GPT-4 for nuanced misuse evaluation. Quantifies both false positives (over-refusal) and false negatives (under-refusal).","intents":["Measure model resistance to jailbreak attempts and adversarial prompts","Quantify toxicity and offensive language generation","Detect if model can be tricked into generating harmful content","Identify over-cautious models that refuse legitimate requests (false positives)"],"best_for":["Teams deploying LLMs in public-facing applications (chatbots, customer service)","Safety researchers studying adversarial robustness and jailbreak techniques","Compliance teams assessing model safety for content moderation policies","Organizations building content filtering systems"],"limitations":["Jailbreak detection is adversarial — new jailbreak techniques may not be covered by existing datasets","Toxicity scoring via Perspective API is imperfect and may have cultural/linguistic biases","Misuse evaluation requires subjective judgment; GPT-4 grading may not align with human safety standards","Exaggerated Safety detection is heuristic-based (RtA rate); doesn't measure quality of refusals"],"requires":["Safety benchmark datasets (included in TrustLLM)","API key for Perspective API (for toxicity scoring)","API key for OpenAI GPT-4 (for misuse evaluation)","Longformer model weights (auto-downloaded from HuggingFace)","Python 3.8+ with trustllm installed"],"input_types":["Model responses (text)","Adversarial prompts and jailbreak attempts","Harmful capability requests","Benign requests (for false positive detection)"],"output_types":["Safety score (0-100)","Per-sub-task scores (jailbreak resistance, toxicity rate, misuse rate, false refusal rate)","Toxicity scores from Perspective API (0-1 scale)","Refusal-to-answer rate and patterns"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_5","uri":"capability://safety.moderation.fairness.evaluation.with.stereotype.disparagement.and.bias.detection","name":"fairness evaluation with stereotype, disparagement, and bias detection","description":"Evaluates model fairness across 4 sub-tasks: Stereotype Recognition (detecting stereotypical associations), Stereotype Agreement (measuring if model endorses stereotypes), Disparagement (offensive language toward groups), and Preference Bias (systematic preference for certain groups). Uses pattern matching for multiple-choice stereotype tasks, Pearson correlation for bias quantification, and GPT-4 for nuanced disparagement evaluation. Measures both implicit bias (learned associations) and explicit bias (overt discrimination).","intents":["Measure if model encodes stereotypes about protected groups (gender, race, religion, etc.)","Detect if model endorses or amplifies stereotypical views","Identify disparaging language toward specific groups","Quantify systematic preference bias in model outputs"],"best_for":["Teams deploying LLMs in high-stakes domains (hiring, lending, criminal justice)","Fairness researchers studying bias in language models","Compliance teams assessing model fairness for anti-discrimination regulations","Organizations building fair recommendation or ranking systems"],"limitations":["Stereotype datasets are culturally and linguistically limited; may not capture all forms of bias","Pearson correlation assumes linear relationships; non-linear biases may be missed","GPT-4 evaluation of disparagement is subjective; may not align with human fairness judgments","Fairness is context-dependent; a model may be fair in one domain but biased in another"],"requires":["Fairness benchmark datasets (included in TrustLLM)","API key for OpenAI GPT-4 (for disparagement evaluation)","Python 3.8+ with trustllm and scipy (for Pearson correlation) installed"],"input_types":["Model responses (text)","Stereotype prompts and reference answers","Disparagement detection prompts","Preference bias test cases"],"output_types":["Fairness score (0-100)","Per-sub-task scores (stereotype recognition, stereotype agreement, disparagement rate, preference bias)","Correlation coefficients for bias quantification","Detailed explanations for biased responses"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_6","uri":"capability://safety.moderation.robustness.evaluation.with.adversarial.examples.and.out.of.distribution.detection","name":"robustness evaluation with adversarial examples and out-of-distribution detection","description":"Evaluates model robustness across 3 sub-tasks: AdvGLUE (adversarial NLU examples), AdvInstruction (adversarial instruction-following), and OOD (out-of-distribution detection and generalization). Uses pattern matching for multiple-choice tasks, deterministic metrics (accuracy, F1) for structured outputs, and heuristic-based OOD detection. Measures performance degradation when inputs are adversarially perturbed or outside the training distribution.","intents":["Measure model performance on adversarially perturbed inputs","Detect if model can identify out-of-distribution examples","Quantify robustness degradation under distribution shift","Benchmark adversarial robustness improvements across model versions"],"best_for":["Teams deploying LLMs in adversarial environments (security, content moderation)","Robustness researchers studying adversarial examples and distribution shift","QA teams testing model behavior on edge cases and unusual inputs","Organizations building robust NLU systems"],"limitations":["AdvGLUE and AdvInstruction datasets are limited in scope; may not cover all adversarial perturbations","OOD detection is heuristic-based; no ground truth for what constitutes 'out-of-distribution'","Adversarial examples may not transfer across models; robustness is model-specific","No evaluation of certified robustness; only empirical robustness on test sets"],"requires":["Robustness benchmark datasets (included in TrustLLM)","Python 3.8+ with trustllm and scikit-learn (for metrics) installed"],"input_types":["Model responses (text)","Adversarial examples (perturbed inputs)","Out-of-distribution test cases","Original (clean) examples for comparison"],"output_types":["Robustness score (0-100)","Per-sub-task scores (AdvGLUE accuracy, AdvInstruction accuracy, OOD detection rate)","Robustness degradation (clean accuracy - adversarial accuracy)","OOD detection metrics (precision, recall, F1)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_7","uri":"capability://safety.moderation.privacy.evaluation.with.awareness.leakage.and.conformity.assessment","name":"privacy evaluation with awareness, leakage, and conformity assessment","description":"Evaluates model privacy across 3 sub-tasks: Privacy Awareness (understanding privacy concepts), Privacy Leakage (extracting sensitive information), and Privacy Conformity (compliance with privacy regulations via ConfAIDe dataset). Uses pattern matching for multiple-choice privacy awareness tasks, heuristic-based leakage detection (e.g., email/phone extraction), and GPT-4 for nuanced conformity evaluation. Measures both privacy knowledge and actual privacy protection.","intents":["Measure if model understands privacy concepts and regulations","Detect if model can be tricked into leaking sensitive information (PII, credentials)","Assess model compliance with privacy regulations (GDPR, CCPA, etc.)","Benchmark privacy improvements across model versions"],"best_for":["Teams deploying LLMs in privacy-sensitive domains (healthcare, finance, legal)","Privacy researchers studying information leakage and privacy attacks","Compliance teams assessing model privacy for regulatory requirements","Organizations building privacy-preserving AI systems"],"limitations":["Privacy leakage detection is heuristic-based; sophisticated leakage may be missed","Privacy awareness is tested via multiple-choice; doesn't measure actual privacy behavior","Privacy conformity evaluation is subjective; GPT-4 grading may not align with legal standards","No evaluation of privacy attacks (membership inference, model inversion); only leakage resistance"],"requires":["Privacy benchmark datasets (included in TrustLLM)","API key for OpenAI GPT-4 (for conformity evaluation)","Python 3.8+ with trustllm installed"],"input_types":["Model responses (text)","Privacy awareness prompts","Privacy leakage attack prompts (e.g., 'What is my email?')","Privacy regulation compliance test cases"],"output_types":["Privacy score (0-100)","Per-sub-task scores (privacy awareness, leakage rate, conformity score)","Types of sensitive information leaked (PII, credentials, etc.)","Privacy regulation compliance assessment"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_8","uri":"capability://safety.moderation.machine.ethics.evaluation.with.explicit.implicit.and.emotional.awareness.assessment","name":"machine ethics evaluation with explicit, implicit, and emotional awareness assessment","description":"Evaluates model ethical reasoning across 3 sub-tasks: Explicit Ethics (understanding ethical principles), Implicit Ethics (ethical behavior in ambiguous situations), and Emotional Awareness (recognizing emotional context and responding empathetically). Uses pattern matching for multiple-choice ethics tasks, GPT-4 for nuanced ethical reasoning evaluation, and heuristic-based emotional awareness scoring. Measures both ethical knowledge and ethical behavior.","intents":["Measure if model understands ethical principles and moral reasoning","Detect if model behaves ethically in ambiguous or complex situations","Assess model emotional intelligence and empathetic response capability","Benchmark ethical improvements across model versions"],"best_for":["Teams deploying LLMs in ethically sensitive domains (counseling, education, social services)","Ethics researchers studying moral reasoning in language models","Organizations building human-aligned AI systems","Compliance teams assessing model ethical behavior for organizational values"],"limitations":["Ethics is culturally and philosophically relative; no universal ground truth for ethical correctness","GPT-4 evaluation of implicit ethics is subjective; may reflect OpenAI's values rather than universal ethics","Emotional awareness is tested via text; doesn't measure actual emotional understanding or empathy","Ethical behavior in controlled benchmarks may not transfer to real-world ethical dilemmas"],"requires":["Machine ethics benchmark datasets (included in TrustLLM)","API key for OpenAI GPT-4 (for implicit ethics evaluation)","Python 3.8+ with trustllm installed"],"input_types":["Model responses (text)","Explicit ethics prompts (ethical principles, moral reasoning)","Implicit ethics scenarios (ambiguous ethical situations)","Emotional awareness prompts (empathy, emotional recognition)"],"output_types":["Ethics score (0-100)","Per-sub-task scores (explicit ethics, implicit ethics, emotional awareness)","Ethical reasoning explanations","Emotional awareness assessment"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__cap_9","uri":"capability://safety.moderation.gpt.4.auto.evaluator.for.open.ended.response.grading","name":"gpt-4 auto-evaluator for open-ended response grading","description":"Implements AutoEvaluator class that uses GPT-4 as a grader for open-ended model responses where pattern matching is insufficient. Sends model responses + evaluation prompts to GPT-4, parses structured outputs (scores, explanations), and aggregates results. Enables flexible evaluation of complex tasks (reasoning, creativity, nuance) without manual annotation. Caches evaluation results to avoid re-querying GPT-4 for identical responses.","intents":["Grade open-ended model responses (essays, explanations, creative content) without manual annotation","Evaluate complex reasoning tasks where multiple correct answers exist","Assess response quality dimensions (clarity, completeness, relevance) via LLM-as-judge","Scale evaluation to thousands of responses without human effort"],"best_for":["Researchers evaluating open-ended generation tasks","Teams benchmarking models on complex reasoning without manual annotation budgets","Organizations scaling evaluation to large response sets","Developers building automated grading systems"],"limitations":["GPT-4 evaluation introduces evaluator bias — results reflect GPT-4's preferences and limitations","Cost scales with response count — GPT-4 API calls are expensive for large benchmarks","Evaluation quality depends on prompt engineering; poorly written evaluation prompts yield poor grades","No ground truth validation — cannot verify if GPT-4 grades align with human judgment without manual spot-checking"],"requires":["API key for OpenAI GPT-4","Evaluation prompts (dimension-specific templates)","Python 3.8+ with trustllm and openai SDK installed","Budget for GPT-4 API calls (~$0.03-0.06 per response depending on length)"],"input_types":["Model responses (text)","Evaluation prompts (instructions for GPT-4 grader)","Reference answers or rubrics (optional)"],"output_types":["Structured evaluation results (scores, explanations, confidence)","Aggregated metrics (mean score, score distribution)","Cached evaluation results (JSON)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trustllm__headline","uri":"capability://testing.quality.trustworthiness.benchmark.for.large.language.models.llms","name":"trustworthiness benchmark for large language models (llms)","description":"TrustLLM is an open-source toolkit designed to evaluate the trustworthiness of Large Language Models across multiple dimensions such as truthfulness, safety, fairness, and ethics, making it essential for developers and researchers focused on LLM reliability.","intents":["best trustworthiness benchmark for LLMs","trustworthiness evaluation tool for AI models","how to assess LLM safety and fairness","LLM trust evaluation framework","open-source tools for evaluating AI ethics"],"best_for":["researchers evaluating AI models","developers ensuring model reliability"],"limitations":[],"requires":["Python environment","access to datasets"],"input_types":["LLM responses"],"output_types":["evaluation metrics","benchmark reports"],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":63,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API keys for target models (OpenAI, Anthropic, Google, etc.) or local HuggingFace model weights","API keys for evaluators (OpenAI GPT-4 for AutoEvaluator, Perspective API for toxicity)","30+ GB disk space for benchmark datasets","Internet connectivity for online model APIs or local GPU for inference","Disk space for JSON response cache (typically 100MB-1GB per model depending on dataset size)","Network connectivity for Stage 1 (generation); 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