| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Provides a registry-based abstraction layer that instantiates language models from 25+ backends (HuggingFace, vLLM, OpenAI, Anthropic, local Ollama, etc.) through a single Python API. The registry pattern decouples task definitions from model implementations, allowing users to swap backends without changing evaluation code. Each backend implements a common interface supporting loglikelihood scoring and text generation with automatic tokenization, BOS token handling, and context window management.
Unique: Uses a pluggable registry system (lm_eval/api/registry.py) where each backend implements a common LM interface with automatic BOS token handling, tokenizer management, and context window validation. Unlike frameworks that require separate evaluation scripts per backend, this centralizes backend logic while preserving backend-specific optimizations (e.g., vLLM's paged attention).
vs alternatives: Supports more backends (25+) than alternatives like LM-Eval-Lite or custom evaluation scripts, and provides unified loglikelihood + generation interface that alternatives often split across separate tools
Enables users to define evaluation tasks declaratively via YAML configuration files with support for Jinja2 templating, task inheritance, and document processing. Tasks specify prompts, few-shot examples, metrics, and answer extraction logic without writing Python code. The TaskManager loads YAML configs, resolves inheritance chains, and instantiates Task objects that generate evaluation requests. This approach separates task logic from evaluation infrastructure, allowing non-engineers to create benchmarks.
Unique: Implements a hierarchical task configuration system where YAML tasks can inherit from parent tasks, override specific fields, and use Jinja2 templating for dynamic prompt generation. The TaskManager resolves inheritance chains and merges configurations, enabling task reuse across 200+ benchmarks. Document processing pipeline (lm_eval/api/task.py) handles dataset loading, few-shot sampling, and prompt rendering in a single pass.
vs alternatives: More declarative and maintainable than hardcoded Python task classes; supports inheritance and templating that alternatives like HELM or LM-Eval-Lite lack, reducing duplication across similar tasks
Enables grouping of related tasks into benchmark suites (e.g., MMLU, BigBench, HELM) with aggregated metrics and reporting. Suites can be defined in YAML or Python, with support for task groups, weighted aggregation, and suite-level metrics. The system computes both per-task and suite-level results, with confidence intervals propagated through aggregation. Supports standard NLP benchmarks, multilingual benchmarks, robustness frameworks (SCORE), and custom suites.
Unique: Provides a declarative suite definition system where tasks can be grouped with optional weights and aggregation methods. The system automatically computes per-task and suite-level metrics, with confidence intervals propagated through aggregation. Supports both standard benchmarks (MMLU, BigBench) and custom suites defined in YAML or Python.
vs alternatives: Supports weighted aggregation and custom suite composition, whereas alternatives typically report only per-task results; integrates suite definition into the evaluation framework rather than requiring external aggregation scripts
Allows advanced users to define evaluation tasks as Python classes extending the Task base class, with custom metric functions and request generation logic. Custom tasks can implement arbitrary evaluation logic beyond YAML capabilities, including complex metrics, multi-stage evaluation, and dynamic request generation. Metrics are registered in a global registry and can be reused across tasks. This provides maximum flexibility for researchers designing novel evaluation approaches.
Unique: Provides a Task base class that users can extend to implement custom evaluation logic, with automatic registration in the global task registry. Custom tasks can override request generation, metric computation, and result aggregation. Metrics are registered separately and can be reused across tasks, enabling modular metric development.
vs alternatives: Enables arbitrary Python logic for task definition and metrics, whereas YAML-based tasks are limited to built-in capabilities; integrates custom tasks into the evaluation pipeline with automatic batching and caching support
Abstracts tokenizer differences across models by providing a unified tokenization interface that handles special tokens, padding, and attention masks consistently. The system automatically selects the correct tokenizer for each model backend and applies model-specific token handling (e.g., BOS token prepending for certain models). This enables fair comparison across models with different tokenization schemes, which would otherwise produce different loglikelihood scores for identical prompts.
Unique: Implements a tokenizer abstraction layer that automatically selects and applies the correct tokenizer for each model backend, with special handling for BOS tokens and model-specific quirks. The system tests BOS token handling empirically (lm_eval/models/test_bos_handling.py) to detect and correct for model-specific behavior, ensuring fair loglikelihood comparison across models.
vs alternatives: Provides automatic BOS token handling and tokenizer selection, whereas alternatives require manual configuration; includes empirical BOS testing to detect model-specific behavior
Provides a comprehensive CLI (lm_eval/__main__.py) that accepts task names, model names, and evaluation parameters as command-line arguments. Supports task filtering (e.g., 'mmlu_*' to run all MMLU variants), model specification with backend selection, and output format configuration. The CLI integrates all framework capabilities (batching, caching, distributed evaluation, logging) without requiring Python code, making the framework accessible to non-programmers.
Unique: Provides a full-featured CLI that exposes all framework capabilities without requiring Python code. Supports task filtering with glob patterns (e.g., 'mmlu_*'), model specification with backend selection, and flexible output configuration. The CLI integrates batching, caching, distributed evaluation, and multi-sink logging.
vs alternatives: More comprehensive CLI than alternatives like simple evaluation scripts; supports task filtering, model selection, and output configuration in a single command
Enables creation of custom benchmark suites by composing multiple tasks and aggregating their metrics into a single leaderboard score. The system supports weighted aggregation (e.g., MMLU counts more than HellaSwag), per-task metric selection, and hierarchical grouping (e.g., 'reasoning' group contains multiple reasoning tasks). Leaderboard scores are computed with optional normalization and ranking.
Unique: Supports weighted aggregation of metrics across multiple tasks with hierarchical grouping. Leaderboard scores are computed with optional normalization, enabling fair comparison across models with different evaluation configurations.
vs alternatives: Compared to manual leaderboard computation, the framework automates aggregation and ranking. Weighted aggregation enables custom benchmark suites tailored to specific evaluation goals.
Implements configurable few-shot sampling strategies that select examples from the training set to include in prompts. Supports random sampling, stratified sampling (balanced across classes), and deterministic seeding for reproducibility. The system caches sampled examples to avoid recomputation and integrates with the request generation pipeline to prepend examples to each evaluation instance. Sampling respects task-specific constraints (e.g., max tokens, example diversity).
Unique: Integrates few-shot sampling directly into the request generation pipeline with built-in caching and stratification support. The system computes sampling once per task, caches results, and reuses them across all evaluation instances. Stratified sampling uses class labels to ensure balanced representation, which is critical for imbalanced datasets where random sampling might miss minority classes.
vs alternatives: Provides stratified sampling (not just random) and automatic caching that alternatives like simple prompt engineering lack; integrates sampling into the evaluation pipeline rather than requiring manual example selection
+7 more capabilities
Generates augmented test suites for MBPP problems by creating 35x more test cases than the original benchmark through systematic edge-case and boundary-condition generation. The system maintains structured metadata for each problem including base_input (original tests), plus_input (extended tests), contract (input validation constraints), atol (floating-point tolerance), canonical_solution (ground truth), and entry_point (function name). This architectural separation enables rigorous detection of fragile solutions that pass shallow tests but fail on edge cases, addressing the fundamental limitation that original MBPP's ~3 tests per task miss correctness issues.
Unique: Provides 35x test case multiplier specifically for MBPP (378 tasks) with structured metadata separation (base_input vs plus_input) and input validation contracts, enabling systematic edge-case coverage that original MBPP's ~3 tests per task cannot achieve. Uses canonical_solution ground truth execution to dynamically calibrate timeouts and floating-point tolerances per problem.
vs alternatives: Significantly more rigorous than original MBPP (3→105 tests per task average) and HumanEval+ (80x multiplier) while maintaining Python-specific focus; catches correctness issues that shallow benchmarks miss but requires more computational resources for evaluation.
Executes arbitrary Python code generated by LLMs in isolated processes with enforced resource limits and system call restrictions to prevent malicious or buggy code from crashing the evaluation framework. The untrusted_check function spawns separate processes via multiprocessing with shared memory IPC, applies memory limits (default 4GB via EVALPLUS_MAX_MEMORY_BYTES environment variable), dynamically calculated time limits based on ground truth execution time, I/O suppression via swallow_io to prevent output pollution, and reliability_guard to disable dangerous system calls. This architecture prevents code injection, infinite loops, memory exhaustion, and filesystem access while maintaining execution fidelity for correctness evaluation.
Implements multi-layer isolation using process-level separation (multiprocessing), memory limits (EVALPLUS_MAX_MEMORY_BYTES), dynamic timeout calculation from canonical_solution execution, I/O suppression (swallow_io), and system call restrictions (reliability_guard). This combination prevents both accidental crashes and intentional attacks while maintaining execution fidelity for correctness evaluation.
MBPP+ scores higher at 64/100 vs lm-evaluation-harness at 56/100.
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vs alternatives: More robust than simple try-catch approaches because it uses OS-level process isolation rather than Python-level exception handling; prevents infinite loops and memory exhaustion that would crash a single-process evaluator, though with higher latency than in-process execution.
Preprocesses LLM-generated code to normalize formatting, remove extraneous content, and extract the target function before execution. The sanitize module (evalplus/sanitize.py) handles variable formatting inconsistencies, removes comments and docstrings that may interfere with parsing, extracts the function matching the entry_point name, and validates syntax before execution. This ensures that evaluation results reflect code correctness rather than formatting quirks or LLM hallucinations like extra imports or wrapper code. The sanitization pipeline is essential because different LLMs produce code with different indentation, naming conventions, and structural patterns that would otherwise cause false negatives.
Unique: Implements multi-stage sanitization pipeline that separates formatting normalization (indentation, whitespace) from structural extraction (entry_point function isolation) and validation (syntax checking). Uses AST-based function extraction rather than regex, ensuring robust handling of complex code structures and nested functions.
vs alternatives: More robust than simple regex-based extraction because it uses Python's ast module for structural parsing; handles edge cases like nested functions, decorators, and complex indentation that regex approaches would miss. Enables fair comparison across LLM models with different output conventions.
Provides unified interface to generate code from 8+ LLM backends including vLLM, HuggingFace, OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Ollama. The provider architecture (evalplus/provider/) abstracts backend-specific API details behind a common interface, handling authentication, request formatting, response parsing, and error handling for each provider. This enables researchers to benchmark code generation across different models and providers without rewriting evaluation code. The codegen module (evalplus/codegen.py) orchestrates the generation pipeline: problem specification → prompt formatting → LLM call → response extraction → sanitization → evaluation.
Unique: Implements provider abstraction layer that unifies 8+ LLM backends (vLLM, HuggingFace, OpenAI, Anthropic, Gemini, Bedrock, Ollama) behind a common interface, enabling single-codebase evaluation across local and cloud models. Each provider handles authentication, request formatting, and response parsing independently, allowing researchers to swap backends without modifying evaluation logic.
vs alternatives: More comprehensive than single-provider frameworks (e.g., OpenAI-only evaluators) because it supports both cloud APIs and self-hosted models; enables cost-benefit analysis between providers and avoids vendor lock-in. Abstraction layer reduces code duplication compared to implementing each provider separately.
Computes pass@k metrics by generating multiple code samples per problem and calculating the probability that at least one sample passes all tests. The metric is calculated as: pass@k = 1 - (C(n-c, k) / C(n, k)) where n is total samples, c is passing samples, and k is the sample count. This enables evaluation of model reliability: pass@1 measures single-shot accuracy, while pass@10 or pass@100 measures whether the model can eventually generate correct code. The framework aggregates results across all problems to produce dataset-level pass@k scores, enabling comparison of models' code generation reliability.
Unique: Implements pass@k metric using combinatorial formula (1 - C(n-c,k)/C(n,k)) rather than empirical sampling, enabling exact calculation without Monte Carlo approximation. Supports configurable k values and aggregation across problems, enabling multi-level analysis (per-problem, per-category, dataset-wide).
vs alternatives: More statistically rigorous than simple accuracy metrics because it accounts for sampling variance and model reliability; enables fair comparison between models with different single-shot accuracy but similar pass@k. Combinatorial calculation is faster and more precise than empirical sampling approaches.
Measures code efficiency using CPU instruction counting rather than wall-clock time, enabling reproducible performance evaluation across different hardware. The EvalPerf dataset generates performance-exercising inputs with exponential scaling (2^1 to 2^26 elements) to stress-test algorithmic complexity. The profiling pipeline uses Linux perf counters to measure CPU instructions, filters tasks based on profile size, compute cost, coefficient of variation, and performance clustering to select representative benchmarks. This approach isolates algorithmic efficiency from hardware variance, enabling rigorous comparison of code quality across models and implementations.
Unique: Uses CPU instruction counting via Linux perf counters rather than wall-clock time, enabling reproducible performance evaluation independent of hardware variance. Generates performance-exercising inputs with exponential scaling (2^1 to 2^26) to stress-test algorithmic complexity, and filters tasks based on profile size, compute cost, and coefficient of variation to select representative benchmarks.
vs alternatives: More reproducible than wall-clock timing because instruction counts are hardware-independent; enables fair comparison across different machines and cloud environments. Exponential input scaling reveals algorithmic complexity issues that constant-size inputs would miss, providing deeper insight into code quality.
Organizes MBPP+ problems as structured JSON with metadata fields: base_input (original test cases), plus_input (extended test cases), contract (input validation constraints), atol (floating-point tolerance), canonical_solution (ground truth implementation), and entry_point (function name). The dataset management system (evalplus/data/) loads problems from JSON, validates metadata consistency, and provides programmatic access to test cases and solutions. This structured approach enables systematic evaluation: problems can be filtered by category, difficulty, or test coverage; test cases can be aggregated across base and plus inputs; and metadata enables reproducible evaluation across different tools and frameworks.
Unique: Implements structured JSON-based dataset organization with explicit separation of base_input (original tests) and plus_input (extended tests), enabling selective evaluation and test coverage analysis. Metadata includes contract (input validation), atol (floating-point tolerance), canonical_solution, and entry_point, providing complete problem specification for reproducible evaluation.
vs alternatives: More structured than flat test files because metadata is explicitly organized and queryable; enables filtering, aggregation, and analysis that would be difficult with unstructured test data. JSON format is human-readable and tool-agnostic, supporting integration with external evaluation frameworks.
Provides CLI tools (evalplus.evaluate, evalplus.codegen, evalplus.evalperf, evalplus.sanitize) that orchestrate the complete evaluation workflow: code generation → sanitization → correctness evaluation → optional performance evaluation. The evaluate command executes generated code against MBPP+ test suites with configurable timeouts and memory limits, producing pass@k metrics and detailed result logs. The codegen command generates code from specified LLM providers. The evalperf command measures performance via instruction counting. The sanitize command preprocesses code before evaluation. This modular CLI design enables researchers to run evaluation pipelines without writing custom code, supporting reproducible benchmarking and result sharing.
Unique: Implements modular CLI tools (evaluate, codegen, evalperf, sanitize) that can be chained together or run independently, enabling flexible evaluation workflows. Each tool handles a specific stage of the pipeline (generation, sanitization, evaluation, performance measurement), allowing users to customize workflows without writing code.
vs alternatives: More user-friendly than programmatic APIs for researchers who prefer command-line tools; enables reproducible evaluation without custom code. Modular design allows selective use of components (e.g., evaluate without codegen) for flexibility.
+2 more capabilities