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| Pricing | Free | Free |
| Capabilities | 15 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
+7 more capabilities
Provides a standardized evaluation framework for code generation models that accepts generated code in 17 programming languages (C, C++, C#, Java, Kotlin, Go, Rust, Python, Ruby, PHP, JavaScript, Perl, Haskell, OCaml, Scala, D, Pascal) and validates correctness through actual execution against unit tests via the ExecEval Docker-based execution engine. Uses a centralized problem definition model with src_uid foreign keys linking generated code to shared problem descriptions and unittest_db.json, enabling consistent evaluation across language variants of the same problem.
Unique: Combines 25M training examples across 7,500 unique problems with an execution-based evaluation pipeline (ExecEval) that actually runs generated code in Docker containers against unit tests, rather than relying on static analysis or string matching. The src_uid linking system creates a normalized data model where problem descriptions and tests are stored once and referenced by all language variants, eliminating duplication and ensuring consistency.
vs alternatives: Larger scale (25M examples vs typical 10-100K) and true execution-based validation across more languages (17 vs 4-6) than HumanEval or CodeXGLUE, with explicit support for code translation and repair tasks beyond generation.
Implements a foreign key linking system where all task-specific datasets (program synthesis, code translation, APR, retrieval) reference shared problem definitions via src_uid identifiers. Problem descriptions and unit tests are stored once in centralized problem_descriptions.jsonl and unittest_db.json files, then linked by src_uid to avoid duplication. The Hugging Face datasets API automatically resolves these links during data loading, returning enriched DatasetDict objects with problem context pre-joined to task examples.
Unique: Uses a normalized relational data model (src_uid as foreign key) for a code benchmark, treating problem definitions as a separate entity layer rather than embedding them in each task dataset. This is more sophisticated than typical flat-file benchmark structures and enables consistent multi-task evaluation on identical problems.
xCodeEval scores higher at 67/100 vs TrustLLM at 64/100.
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vs alternatives: More efficient than duplicating problem descriptions across 7 task datasets (reduces storage by ~30-40%), and enables automatic link resolution via Hugging Face API unlike manual CSV joins in CodeXGLUE or HumanEval variants.
Provides a Python API for loading xCodeEval datasets from Hugging Face Hub (NTU-NLP-sg/xCodeEval) with automatic src_uid-based linking between task datasets and shared problem definitions. The datasets library handles data downloading, caching, and streaming, while the xCodeEval integration automatically joins task examples with problem_descriptions.jsonl and unittest_db.json using src_uid foreign keys. Returns DatasetDict objects with enriched examples ready for model training or evaluation.
Unique: Integrates xCodeEval with Hugging Face datasets library, providing automatic src_uid resolution and streaming support. Treats data loading as a first-class concern with built-in linking logic, rather than requiring manual JSON parsing.
vs alternatives: More convenient than manual Git LFS downloads because it handles caching and automatic linking, and integrates seamlessly with Hugging Face training pipelines vs custom data loaders.
Provides an alternative data access method using Git LFS for users who prefer direct file access or need selective dataset downloads. Supports cloning the repository with LFS disabled, then pulling specific task files or problem definitions on demand. Useful for custom processing pipelines or environments where Python/Hugging Face is not available, though requires manual src_uid linking to join task examples with problem definitions.
Unique: Provides Git LFS-based alternative to Hugging Face API, enabling direct file access and selective downloads. Requires manual src_uid linking but offers more control over data access patterns.
vs alternatives: More flexible than Hugging Face API for selective downloads and custom pipelines, but requires more manual work for src_uid linking and lacks automatic caching/streaming.
Implements a standardized three-phase evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) that applies consistently across all 7 tasks (program synthesis, code translation, APR, tag classification, code compilation, NL-code retrieval, code-code retrieval). Phase 1 generates or retrieves code, Phase 2 executes it via ExecEval or computes retrieval metrics, and Phase 3 aggregates results into pass@k, MRR, NDCG, or other task-specific metrics. Enables direct comparison of model performance across tasks.
Unique: Defines a unified three-phase evaluation pipeline that applies to all 7 tasks, treating generation, execution, and metric computation as separate concerns. Enables consistent evaluation methodology across diverse task types (generation, translation, retrieval, classification).
vs alternatives: More comprehensive than task-specific evaluation scripts because it provides a unified framework for all 7 tasks, and enables direct comparison of model performance across different task types.
Evaluates code generation models on the program synthesis task by accepting natural language problem descriptions and generating code solutions in any of 17 languages. The evaluation pipeline (Phase 1: Generation, Phase 2: Execution, Phase 3: Metrics) runs generated code against unit tests via ExecEval, computing pass@k metrics (pass@1, pass@10, etc.) that measure the probability of finding a correct solution within k samples. Supports both single-solution and multi-sample evaluation modes for assessing model reliability.
Unique: Implements a three-phase evaluation pipeline (Generation → Execution → Metrics) with explicit pass@k computation that measures the probability of finding a correct solution within k attempts, rather than just binary pass/fail. Supports multi-sample evaluation across 17 languages with language-specific compiler configurations and timeout handling.
vs alternatives: More rigorous than HumanEval's simple pass@k because it handles language-specific compilation errors and timeouts explicitly, and scales to 25M training examples vs HumanEval's 164 problems.
Evaluates code translation models by accepting source code in one language and generated translations in a target language, then validating functional equivalence through execution against shared unit tests. The translation evaluation pipeline compiles and executes both source and translated code against the same unittest_db.json test cases, comparing outputs to detect translation errors. Supports all 17 language pairs (though not all pairs may have training data) and uses language-specific compiler mappings to handle syntax differences.
Unique: Validates code translation by executing both source and target code against identical unit tests and comparing outputs, ensuring functional equivalence rather than syntactic similarity. Uses language-specific compiler mappings to handle the complexity of 17 different compilation environments and their idiosyncrasies.
vs alternatives: More rigorous than BLEU-score-based translation metrics because it validates actual functional correctness through execution, and covers more language pairs (17 vs typical 2-4) with explicit compiler integration.
Evaluates program repair models by providing buggy code snippets and expecting corrected versions that pass unit tests. The APR evaluation pipeline executes repaired code against unittest_db.json test cases, measuring whether the repair successfully fixes the bug without introducing new failures. Supports repairs across all 17 languages and uses the same execution-based validation as program synthesis, enabling direct comparison of repair quality.
Unique: Treats program repair as an executable task where success is measured by unit test passage, rather than syntactic similarity to reference repairs. Integrates with the same ExecEval pipeline as program synthesis, enabling direct performance comparison between generation and repair models.
vs alternatives: More comprehensive than traditional APR benchmarks (Defects4J, QuixBugs) because it covers 17 languages and 7,500 problems vs 395 Java bugs, and uses consistent execution-based metrics across all repair types.
+5 more capabilities