What can TrustLLM do?

multi-dimensional trustworthiness evaluation across 6 core dimensions, two-stage generation-then-evaluation pipeline orchestration, longformer-based toxicity classification for safety evaluation, perspective api integration for external toxicity scoring, multi-model comparative ranking and leaderboard generation, dataset management and benchmark curation with 30+ integrated datasets, configuration-driven model and evaluator routing, unified model backend abstraction for online and local inference, truthfulness evaluation with misinformation, hallucination, and sycophancy detection, safety evaluation with jailbreak, toxicity, and misuse detection, fairness evaluation with stereotype, disparagement, and bias detection, robustness evaluation with adversarial examples and out-of-distribution detection, privacy evaluation with awareness, leakage, and conformity assessment, machine ethics evaluation with explicit, implicit, and emotional awareness assessment, gpt-4 auto-evaluator for open-ended response grading

TrustLLM

BenchmarkFree

8-dimension trustworthiness benchmark for LLMs.

Open Source

/ 100

15 capabilities

Capabilities15 decomposed

multi-dimensional trustworthiness evaluation across 6 core dimensions

Medium confidence

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.

Solves for

Compare trustworthiness profiles of multiple LLMs across safety, fairness, and ethics dimensionsIdentify which trustworthiness dimensions a model fails on before production deploymentBenchmark custom fine-tuned models against public LLMs using standardized evaluation criteriaTrack 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

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)

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

What makes it unique

Combines 6 orthogonal trustworthiness dimensions (not just safety or factuality) with 30+ datasets and mixed evaluation strategies (pattern matching, LLM-as-judge, deterministic metrics, external APIs). Supports both online and local model backends with unified configuration, enabling fair comparison across proprietary and open-source models in a single benchmark run.

vs alternatives

More comprehensive than single-dimension benchmarks (e.g., TruthfulQA for truthfulness only) and more accessible than custom evaluation pipelines because it bundles datasets, evaluators, and reporting in one framework.

two-stage generation-then-evaluation pipeline orchestration

Medium confidence

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.

Solves for

Run expensive model inference once and evaluate multiple times with different metrics without re-queryingParallelize API calls to multiple online models to reduce total benchmark runtimeReproduce evaluation results deterministically by storing and replaying cached model outputsSwap 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

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

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

What makes it unique

Decouples inference from evaluation with explicit caching, allowing cost-efficient re-evaluation and metric iteration. Uses GROUP_SIZE-based multi-threading for parallel API calls rather than async/await, making it simpler to reason about concurrency limits and rate-limiting per provider.

vs alternatives

More cost-effective than frameworks that re-query models for each evaluation metric, and more reproducible than end-to-end pipelines that don't cache intermediate responses.

longformer-based toxicity classification for safety evaluation

Medium confidence

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.

Solves for

Score model responses for toxicity and offensive language without API callsEvaluate toxicity at scale with low latency and zero API costProvide local alternative to Perspective API for privacy-sensitive deploymentsDetect 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

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)

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

What makes it unique

Uses Longformer (efficient transformer for long sequences) for local toxicity classification, avoiding external API dependencies. Enables batch processing for cost-free, privacy-preserving toxicity evaluation.

vs alternatives

Faster and cheaper than Perspective API for large-scale evaluation, though potentially less accurate due to dataset-specific training.

perspective api integration for external toxicity scoring

Medium confidence

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.

Solves for

Score model responses using industry-standard toxicity detection (Perspective API)Cross-validate local toxicity classifiers against external ground truthMeasure 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

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

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

What makes it unique

Integrates Google's Perspective API for external toxicity validation, enabling cross-checking against industry-standard toxicity detection. Provides multiple toxicity dimensions (toxicity, severe toxicity, profanity) rather than single toxicity score.

vs alternatives

More authoritative than local classifiers because it uses Google's widely-adopted toxicity standards, though slower and rate-limited compared to local evaluation.

multi-model comparative ranking and leaderboard generation

Medium confidence

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.

Solves for

Compare trustworthiness profiles of multiple models in a single leaderboardIdentify which models excel in specific dimensions (e.g., high safety but low fairness)Track model performance over time as new versions are releasedPublish 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

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)

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

What makes it unique

Generates multi-dimensional leaderboards that show per-dimension scores and overall rankings, enabling nuanced comparison rather than single-metric ranking. Supports customizable dimension weighting for different use cases.

vs alternatives

More informative than single-metric leaderboards because it shows trade-offs across dimensions (e.g., a model may be safe but unfair), helping stakeholders make context-aware decisions.

dataset management and benchmark curation with 30+ integrated datasets

Medium confidence

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.

Solves for

Access standardized benchmark datasets without manual curationFilter datasets by dimension, domain, or language for targeted evaluationReproduce benchmarks using pinned dataset versionsExtend 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

Requires

TrustLLM package with datasets (30+ GB total)

Python 3.8+ with trustllm installed

Internet connectivity to download datasets (one-time)

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

What makes it unique

Bundles 30+ curated datasets across 6 trustworthiness dimensions with standardized format and metadata, enabling one-command access to comprehensive benchmarks. Supports dataset versioning for reproducibility.

vs alternatives

More convenient than assembling datasets from multiple sources because it provides integrated, standardized datasets with metadata and filtering utilities.

configuration-driven model and evaluator routing

Medium confidence

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).

Solves for

Switch between models or evaluators by changing configuration filesManage API credentials securely via environment variablesDeploy TrustLLM across multiple environments with different model/evaluator selectionsEnable 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

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

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

What makes it unique

Centralizes model and evaluator configuration in JSON/Python files with environment variable overrides, enabling configuration-driven routing without code changes. Supports multi-environment deployment patterns.

vs alternatives

More flexible than hardcoded model selection and more accessible than programmatic configuration because it enables non-technical users to configure benchmarks.

unified model backend abstraction for online and local inference

Medium confidence

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.

Solves for

Benchmark proprietary models (GPT-4, Claude) and open-source models (Llama, Mistral) in the same evaluation runSwitch between cloud APIs and local inference without modifying benchmark codeTest custom fine-tuned models deployed locally without exposing them to external APIsCompare 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

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

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

What makes it unique

Single unified interface (LLMGeneration) abstracts both online APIs and local models, with configuration-driven routing via model_info.json. Handles credential management, request formatting, and response normalization for 6+ online providers and local HuggingFace/fastchat backends without requiring provider-specific code.

vs alternatives

More flexible than provider-specific SDKs and more standardized than ad-hoc wrapper scripts because it enforces consistent configuration and response formats across all backends.

truthfulness evaluation with misinformation, hallucination, and sycophancy detection

Medium confidence

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.

Solves for

Measure how often a model generates false or contradictory informationDetect 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

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

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

What makes it unique

Combines multiple factuality signals (internal consistency, external accuracy, hallucination, agreement bias) into a single truthfulness dimension. Uses mixed evaluation strategies: pattern matching for structured tasks, GPT-4 for open-ended grading, and deterministic metrics for reproducibility.

vs alternatives

More comprehensive than single-metric factuality benchmarks (e.g., TruthfulQA alone) because it captures hallucination, sycophancy, and internal contradictions in addition to external factuality.

safety evaluation with jailbreak, toxicity, and misuse detection

Medium confidence

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).

Solves for

Measure model resistance to jailbreak attempts and adversarial promptsQuantify toxicity and offensive language generationDetect if model can be tricked into generating harmful contentIdentify 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

Requires

Safety benchmark datasets (included in TrustLLM)

API key for Perspective API (for toxicity scoring)

API key for OpenAI GPT-4 (for misuse evaluation)

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

What makes it unique

Evaluates both false negatives (harmful outputs) and false positives (over-refusal), using a mix of external APIs (Perspective), classifiers (Longformer), and LLM-as-judge (GPT-4). Captures nuanced safety trade-offs rather than binary safe/unsafe classification.

vs alternatives

More balanced than safety benchmarks focused only on refusal rate because it measures both under-refusal (safety failures) and over-refusal (usability failures).

fairness evaluation with stereotype, disparagement, and bias detection

Medium confidence

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).

Solves for

Measure if model encodes stereotypes about protected groups (gender, race, religion, etc.)Detect if model endorses or amplifies stereotypical viewsIdentify disparaging language toward specific groupsQuantify 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

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

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

What makes it unique

Separates stereotype recognition (detecting associations) from stereotype agreement (endorsing associations), capturing both implicit and explicit bias. Uses Pearson correlation for quantifying systematic preference bias rather than binary bias/no-bias classification.

vs alternatives

More nuanced than single-metric bias benchmarks because it measures multiple fairness dimensions (recognition, agreement, disparagement, preference) and distinguishes between detecting bias and endorsing bias.

robustness evaluation with adversarial examples and out-of-distribution detection

Medium confidence

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.

Solves for

Measure model performance on adversarially perturbed inputsDetect if model can identify out-of-distribution examplesQuantify robustness degradation under distribution shiftBenchmark 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

Requires

Robustness benchmark datasets (included in TrustLLM)

Python 3.8+ with trustllm and scikit-learn (for metrics) installed

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

What makes it unique

Combines adversarial NLU (AdvGLUE), adversarial instruction-following (AdvInstruction), and OOD detection into a single robustness dimension. Uses deterministic metrics for reproducibility while capturing both adversarial and distributional robustness.

vs alternatives

More comprehensive than single-adversarial-dataset benchmarks because it measures robustness to multiple perturbation types and includes OOD detection, which is critical for real-world deployment.

privacy evaluation with awareness, leakage, and conformity assessment

Medium confidence

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.

Solves for

Measure if model understands privacy concepts and regulationsDetect 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

Requires

Privacy benchmark datasets (included in TrustLLM)

API key for OpenAI GPT-4 (for conformity evaluation)

Python 3.8+ with trustllm installed

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

What makes it unique

Combines privacy knowledge (awareness), privacy behavior (leakage resistance), and privacy compliance (regulatory conformity) into a single dimension. Uses mixed evaluation strategies: pattern matching for awareness, heuristics for leakage, and LLM-as-judge for conformity.

vs alternatives

More holistic than privacy benchmarks focused only on leakage because it measures privacy understanding, actual protection, and regulatory compliance.

machine ethics evaluation with explicit, implicit, and emotional awareness assessment

Medium confidence

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.

Solves for

Measure if model understands ethical principles and moral reasoningDetect if model behaves ethically in ambiguous or complex situationsAssess model emotional intelligence and empathetic response capabilityBenchmark 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

Requires

Machine ethics benchmark datasets (included in TrustLLM)

API key for OpenAI GPT-4 (for implicit ethics evaluation)

Python 3.8+ with trustllm installed

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

What makes it unique

Combines ethical knowledge (explicit ethics), ethical behavior (implicit ethics), and emotional intelligence (emotional awareness) into a single ethics dimension. Uses GPT-4 for nuanced reasoning evaluation rather than pattern matching, acknowledging the subjective nature of ethics.

vs alternatives

More comprehensive than single-metric ethics benchmarks because it measures ethical knowledge, ethical behavior, and emotional awareness, capturing multiple facets of ethical AI.

gpt-4 auto-evaluator for open-ended response grading

Medium confidence

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.

Solves for

Grade open-ended model responses (essays, explanations, creative content) without manual annotationEvaluate complex reasoning tasks where multiple correct answers existAssess response quality dimensions (clarity, completeness, relevance) via LLM-as-judgeScale 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

Requires

API key for OpenAI GPT-4

Evaluation prompts (dimension-specific templates)

Python 3.8+ with trustllm and openai SDK installed

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

What makes it unique

Uses GPT-4 as a flexible evaluator for open-ended tasks, with caching to avoid redundant API calls. Parses structured outputs from GPT-4 to enable programmatic aggregation and comparison across models.

vs alternatives

More flexible than pattern-matching evaluators for complex tasks, and more cost-efficient than manual annotation, though introduces evaluator bias that pattern matching avoids.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with TrustLLM, ranked by overlap. Discovered automatically through the match graph.

Dataset59

RealToxicityPrompts

100K prompts for evaluating toxic text generation.

multi-dimensional toxicity scoring for prompt-completion pairstoxicity-based model evaluation benchmarking

2 shared capabilities

Product56

Patronus AI

Enterprise LLM evaluation for hallucination and safety.

multi-evaluator-chaining-and-aggregationtoxicity-and-safety-content-filtering

2 shared capabilities

Dataset60

ToxiGen

Microsoft's dataset for implicit toxicity detection.

multi-group-toxicity-dataset-generation-across-13-minoritiespretrained-toxicity-classifier-integration

2 shared capabilities

Benchmark34

VBench

[CVPR2024 Highlight] VBench - We Evaluate Video Generation

trustworthiness and safety evaluation for video generation models

1 shared capability

Product25

DJD Agent Score

Reputation scoring for AI agent wallets on Base L2. Check trust scores (0-100) across 5 dimensions before transacting with autonomous agents. Free tier available.

dimension-based trust evaluation

1 shared capability

Benchmark62

HELM

Stanford's holistic LLM evaluation — 42 scenarios, 7 metrics including fairness, bias, toxicity.

toxicity and harmful content detection in model outputs

1 shared capability

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
✓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

Known 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
⚠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

Requirements

Python 3.8+API keys for target models (OpenAI, Anthropic, Google, etc.) or local HuggingFace model weightsAPI keys for evaluators (OpenAI GPT-4 for AutoEvaluator, Perspective API for toxicity)30+ GB disk space for benchmark datasetsInternet connectivity for online model APIs or local GPU for inferenceDisk 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 evaluatorsPython 3.8+ with trustllm package installed

Input / Output

Accepts: 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), 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), Model responses (text), Batch size configuration (for efficiency tuning), Model responses (text, up to 20K characters per request), Evaluation results (JSON files with per-dimension scores), Model metadata (model name, provider, version), Dataset queries (dimension, domain, language filters), Custom dataset files (JSON format), Configuration files (JSON, Python), Environment variables, Model configuration (provider name, model ID, API endpoint, inference parameters), Prompts and benchmark datasets, API credentials (keys, tokens, endpoints), Prompts and reference answers, Ground truth datasets (JSON with questions, correct answers, distractors), Adversarial prompts and jailbreak attempts, Harmful capability requests, Benign requests (for false positive detection), Stereotype prompts and reference answers, Disparagement detection prompts, Preference bias test cases, Adversarial examples (perturbed inputs), Out-of-distribution test cases, Original (clean) examples for comparison, Privacy awareness prompts, Privacy leakage attack prompts (e.g., 'What is my email?'), Privacy regulation compliance test cases, Explicit ethics prompts (ethical principles, moral reasoning), Implicit ethics scenarios (ambiguous ethical situations), Emotional awareness prompts (empathy, emotional recognition), Evaluation prompts (instructions for GPT-4 grader), Reference answers or rubrics (optional)

Produces: 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, Stage 1: JSON files with model responses, timestamps, and metadata, Stage 2: Evaluation scores, per-sample explanations, aggregated metrics, leaderboard rankings, Toxicity scores (0-1 scale per response), Batch aggregation (mean toxicity, distribution), Toxicity scores (0-1 scale for multiple attributes: toxicity, severe toxicity, profanity, etc.), Aggregated metrics (mean toxicity, distribution), Leaderboard data (JSON, CSV with rankings and scores), Visualizations (rank cards, score distributions, heatmaps), Summary statistics (mean scores, score ranges), Loaded datasets (Python objects with prompts, metadata, expected outputs), Dataset statistics (size, domain distribution, language distribution), Filtered/split datasets for evaluation, Resolved configuration (model provider, evaluator selection, credentials), Normalized model responses (text, tokens, logits where available), Metadata (latency, token count, cost estimate, provider), 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, 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, 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, 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), 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, Ethics score (0-100), Per-sub-task scores (explicit ethics, implicit ethics, emotional awareness), Ethical reasoning explanations, Emotional awareness assessment, Structured evaluation results (scores, explanations, confidence), Aggregated metrics (mean score, score distribution), Cached evaluation results (JSON)

UnfragileRank

Adoption70%(25% weight)

Quality90%(35% weight)

Ecosystem30%(15% weight)

Match Graph25%(20% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Benchmark

15 capabilities

Visit TrustLLM→

About

Comprehensive trustworthiness benchmark evaluating LLMs across 8 dimensions including truthfulness, safety, fairness, robustness, privacy, machine ethics, transparency, and accountability with 30+ datasets.

Alternatives to TrustLLM

v087Product

AI UI generator by Vercel — creates production-quality React/Next.js components from natural language descriptions.

Compare →

Framer82Product

AI-powered website design and publishing — generates responsive, professionally designed sites from descriptions.

Compare →

Midjourney79Product

AI image generation — artistic high-quality outputs, Discord bot, photorealistic V6 model.

Compare →

xCodeEval67Benchmark

Multilingual code evaluation across 17 languages.

Compare →

Are you the builder of TrustLLM?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

seed developer essentials

Looking for something else?

Search →

Capabilities15 decomposed

multi-dimensional trustworthiness evaluation across 6 core dimensions

Medium confidence

Solves for

Best for

AI safety researchers evaluating model reliability

Enterprise teams vetting LLMs for regulated industries (finance, healthcare, legal)

Model developers building trustworthy AI systems

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)

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

What makes it unique

vs alternatives

two-stage generation-then-evaluation pipeline orchestration

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

More cost-effective than frameworks that re-query models for each evaluation metric, and more reproducible than end-to-end pipelines that don't cache intermediate responses.

longformer-based toxicity classification for safety evaluation

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

Faster and cheaper than Perspective API for large-scale evaluation, though potentially less accurate due to dataset-specific training.

perspective api integration for external toxicity scoring

Medium confidence

Solves for

Best for

Teams requiring industry-standard toxicity scoring

Organizations validating local toxicity classifiers against external benchmarks

Researchers studying toxicity detection accuracy

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

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

What makes it unique

vs alternatives

More authoritative than local classifiers because it uses Google's widely-adopted toxicity standards, though slower and rate-limited compared to local evaluation.

multi-model comparative ranking and leaderboard generation

Medium confidence

Solves for

Best for

Organizations comparing 3+ models for deployment decisions

Researchers publishing model benchmarks and leaderboards

Teams tracking model performance improvements across versions

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)

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

What makes it unique

vs alternatives

More informative than single-metric leaderboards because it shows trade-offs across dimensions (e.g., a model may be safe but unfair), helping stakeholders make context-aware decisions.

dataset management and benchmark curation with 30+ integrated datasets

Medium confidence

Solves for

Best for

Researchers benchmarking models without building custom datasets

Teams standardizing on TrustLLM datasets for reproducible comparisons

Organizations extending TrustLLM with domain-specific datasets

Requires

TrustLLM package with datasets (30+ GB total)

Python 3.8+ with trustllm installed

Internet connectivity to download datasets (one-time)

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

What makes it unique

vs alternatives

More convenient than assembling datasets from multiple sources because it provides integrated, standardized datasets with metadata and filtering utilities.

configuration-driven model and evaluator routing

Medium confidence

Solves for

Best for

Teams managing multiple models and evaluators

Organizations deploying TrustLLM in multiple environments

Developers building configuration-driven evaluation pipelines

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

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

What makes it unique

vs alternatives

More flexible than hardcoded model selection and more accessible than programmatic configuration because it enables non-technical users to configure benchmarks.

unified model backend abstraction for online and local inference

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

More flexible than provider-specific SDKs and more standardized than ad-hoc wrapper scripts because it enforces consistent configuration and response formats across all backends.

truthfulness evaluation with misinformation, hallucination, and sycophancy detection

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

More comprehensive than single-metric factuality benchmarks (e.g., TruthfulQA alone) because it captures hallucination, sycophancy, and internal contradictions in addition to external factuality.

safety evaluation with jailbreak, toxicity, and misuse detection

Medium confidence

Solves for

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

Requires

Safety benchmark datasets (included in TrustLLM)

API key for Perspective API (for toxicity scoring)

API key for OpenAI GPT-4 (for misuse evaluation)

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

What makes it unique

vs alternatives

More balanced than safety benchmarks focused only on refusal rate because it measures both under-refusal (safety failures) and over-refusal (usability failures).

fairness evaluation with stereotype, disparagement, and bias detection

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

robustness evaluation with adversarial examples and out-of-distribution detection

Medium confidence

Solves for

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

Requires

Robustness benchmark datasets (included in TrustLLM)

Python 3.8+ with trustllm and scikit-learn (for metrics) installed

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

What makes it unique

vs alternatives

More comprehensive than single-adversarial-dataset benchmarks because it measures robustness to multiple perturbation types and includes OOD detection, which is critical for real-world deployment.

privacy evaluation with awareness, leakage, and conformity assessment

Medium confidence

Solves for

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

Requires

Privacy benchmark datasets (included in TrustLLM)

API key for OpenAI GPT-4 (for conformity evaluation)

Python 3.8+ with trustllm installed

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

What makes it unique

vs alternatives

More holistic than privacy benchmarks focused only on leakage because it measures privacy understanding, actual protection, and regulatory compliance.

machine ethics evaluation with explicit, implicit, and emotional awareness assessment

Medium confidence

Solves for

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

Requires

Machine ethics benchmark datasets (included in TrustLLM)

API key for OpenAI GPT-4 (for implicit ethics evaluation)

Python 3.8+ with trustllm installed

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

What makes it unique

vs alternatives

More comprehensive than single-metric ethics benchmarks because it measures ethical knowledge, ethical behavior, and emotional awareness, capturing multiple facets of ethical AI.

gpt-4 auto-evaluator for open-ended response grading

Medium confidence

Solves for

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

Requires

API key for OpenAI GPT-4

Evaluation prompts (dimension-specific templates)

Python 3.8+ with trustllm and openai SDK installed

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

What makes it unique

vs alternatives

More flexible than pattern-matching evaluators for complex tasks, and more cost-efficient than manual annotation, though introduces evaluator bias that pattern matching avoids.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Alternatives to TrustLLM

v087Product

AI UI generator by Vercel — creates production-quality React/Next.js components from natural language descriptions.

Compare →

Framer82Product

AI-powered website design and publishing — generates responsive, professionally designed sites from descriptions.

Compare →

Midjourney79Product

AI image generation — artistic high-quality outputs, Discord bot, photorealistic V6 model.

Compare →

xCodeEval67Benchmark

Multilingual code evaluation across 17 languages.

Compare →

TrustLLM

Capabilities15 decomposed

multi-dimensional trustworthiness evaluation across 6 core dimensions

two-stage generation-then-evaluation pipeline orchestration

longformer-based toxicity classification for safety evaluation

perspective api integration for external toxicity scoring

multi-model comparative ranking and leaderboard generation

dataset management and benchmark curation with 30+ integrated datasets

configuration-driven model and evaluator routing

unified model backend abstraction for online and local inference

truthfulness evaluation with misinformation, hallucination, and sycophancy detection

safety evaluation with jailbreak, toxicity, and misuse detection

fairness evaluation with stereotype, disparagement, and bias detection

robustness evaluation with adversarial examples and out-of-distribution detection

privacy evaluation with awareness, leakage, and conformity assessment

machine ethics evaluation with explicit, implicit, and emotional awareness assessment

gpt-4 auto-evaluator for open-ended response grading

Related Artifactssharing capabilities

RealToxicityPrompts

Patronus AI

ToxiGen

VBench

DJD Agent Score

HELM

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to TrustLLM

Are you the builder of TrustLLM?

Get the weekly brief

Data Sources

TrustLLM

Capabilities15 decomposed

multi-dimensional trustworthiness evaluation across 6 core dimensions

two-stage generation-then-evaluation pipeline orchestration

longformer-based toxicity classification for safety evaluation

perspective api integration for external toxicity scoring

multi-model comparative ranking and leaderboard generation

dataset management and benchmark curation with 30+ integrated datasets

configuration-driven model and evaluator routing

unified model backend abstraction for online and local inference

truthfulness evaluation with misinformation, hallucination, and sycophancy detection

safety evaluation with jailbreak, toxicity, and misuse detection

fairness evaluation with stereotype, disparagement, and bias detection

robustness evaluation with adversarial examples and out-of-distribution detection

privacy evaluation with awareness, leakage, and conformity assessment

machine ethics evaluation with explicit, implicit, and emotional awareness assessment

gpt-4 auto-evaluator for open-ended response grading

Related Artifactssharing capabilities

RealToxicityPrompts

Patronus AI

ToxiGen

VBench

DJD Agent Score

HELM

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to TrustLLM

Are you the builder of TrustLLM?

Get the weekly brief

Data Sources