ZeroEval
BenchmarkFreeZero-shot LLM evaluation for reasoning tasks.
Capabilities10 decomposed
zero-shot mathematical reasoning evaluation
Medium confidenceEvaluates LLM performance on mathematical reasoning tasks without few-shot examples by implementing standardized prompt templates and answer extraction patterns. The framework parses model outputs to extract numerical answers and compares them against ground truth using exact match and approximate numerical matching (within configurable tolerance thresholds), enabling fair assessment of raw mathematical capability without demonstration-based priming.
Implements unified zero-shot evaluation protocol specifically designed to eliminate few-shot demonstration bias, using standardized answer extraction heuristics (regex patterns, numerical parsing) that work across diverse mathematical problem formats without requiring task-specific prompt engineering
Differs from MATH and GSM8K benchmarks by enforcing strict zero-shot conditions and providing unified evaluation harness across multiple mathematical domains rather than single-domain focus
logical deduction task evaluation with structured reasoning
Medium confidenceAssesses LLM performance on logical deduction problems (e.g., syllogisms, constraint satisfaction, formal logic) by parsing model outputs for logical conclusions and validating them against ground truth using symbolic logic matching. The framework handles multi-step deduction chains and validates intermediate reasoning steps, not just final conclusions, enabling detection of correct-by-accident answers versus sound logical reasoning.
Validates both final answers and intermediate reasoning steps in logical deduction chains, using symbolic matching to detect correct conclusions reached through invalid reasoning paths, rather than only checking answer correctness
Goes beyond simple answer matching used in most benchmarks by implementing reasoning chain validation, enabling detection of spurious correctness and assessment of logical soundness rather than just accuracy
code generation evaluation with execution-based verification
Medium confidenceEvaluates LLM-generated code by executing it against test cases and comparing outputs to expected results, supporting multiple programming languages (Python, JavaScript, Java, C++, etc.). The framework sandboxes code execution to prevent malicious or infinite-loop code from crashing the evaluation process, captures stdout/stderr, and provides detailed pass/fail metrics per test case with execution time and memory usage tracking.
Implements execution-based verification with language-agnostic test harness that safely executes generated code in isolated environments, capturing detailed execution metrics (runtime, memory, stdout/stderr) rather than relying on string matching or static analysis
Provides more reliable code quality assessment than HumanEval's simple output matching by executing code and validating against comprehensive test suites, while supporting more languages than most single-language benchmarks
unified evaluation protocol orchestration
Medium confidenceProvides a unified framework that orchestrates evaluation across heterogeneous task types (math, logic, code) using a common interface and configuration system. The framework abstracts task-specific evaluation logic behind a standardized evaluator interface, allowing users to define custom evaluation metrics, configure model parameters, and run batch evaluations across multiple models and datasets with a single configuration file, reducing boilerplate and ensuring consistent evaluation methodology.
Implements a task-agnostic evaluator interface that abstracts domain-specific evaluation logic, allowing unified batch evaluation across math, logic, and code tasks through a single configuration-driven pipeline rather than separate task-specific scripts
Consolidates evaluation of multiple reasoning domains into one framework with consistent configuration and reporting, whereas most benchmarks focus on single task types or require separate evaluation pipelines per domain
model-agnostic evaluation with multi-provider support
Medium confidenceSupports evaluation of LLMs from multiple providers (OpenAI, Anthropic, Hugging Face, local Ollama instances) through a unified model interface that abstracts provider-specific API differences. The framework handles authentication, rate limiting, retry logic, and response parsing for each provider, allowing users to benchmark models across different providers without rewriting evaluation code, and supports both API-based and locally-hosted models.
Implements a provider-agnostic model interface that abstracts OpenAI, Anthropic, Hugging Face, and local Ollama APIs behind a unified interface, handling authentication, rate limiting, and response parsing differences automatically rather than requiring provider-specific evaluation code
Enables seamless cross-provider model comparison without rewriting evaluation logic, whereas most benchmarks are tied to specific model APIs or require manual adaptation for each provider
dataset standardization and format conversion
Medium confidenceProvides utilities to standardize evaluation datasets across different formats (JSON, JSONL, CSV, HuggingFace datasets) into a unified internal representation with schema validation. The framework validates dataset structure, detects missing fields, handles encoding issues, and converts between formats, ensuring consistent data ingestion regardless of source format and enabling reuse of datasets across different evaluation tasks.
Implements schema-based dataset validation and format conversion that normalizes heterogeneous data sources (JSON, JSONL, CSV, HuggingFace) into a unified internal representation with explicit field mapping and validation rules
Provides centralized dataset handling with format-agnostic validation, whereas most benchmarks assume specific input formats or require manual dataset preparation
zero-shot prompt template management
Medium confidenceManages standardized prompt templates for zero-shot evaluation that eliminate few-shot demonstration bias by enforcing strict prompt structures without examples. The framework provides task-specific prompt templates (math, logic, code) with configurable instructions and output format specifications, validates that generated prompts contain no few-shot examples, and allows users to define custom templates while maintaining zero-shot constraints.
Implements explicit zero-shot prompt template validation that detects and prevents few-shot example contamination, using template structure analysis and content validation rules to enforce strict zero-shot methodology
Provides explicit zero-shot enforcement through template validation, whereas most benchmarks rely on manual prompt discipline without automated safeguards against few-shot contamination
evaluation metrics computation and aggregation
Medium confidenceComputes standardized evaluation metrics (accuracy, precision, recall, F1, BLEU, exact match, etc.) for each task type and aggregates results across multiple dimensions (per-model, per-dataset, per-task-category). The framework supports custom metric definitions, handles edge cases (division by zero, missing predictions), and generates comparative statistics (mean, std dev, confidence intervals) enabling statistical significance testing and detailed performance analysis.
Implements task-agnostic metric computation with support for custom metric definitions and multi-dimensional aggregation (per-model, per-dataset, per-category), enabling flexible performance analysis across heterogeneous evaluation tasks
Provides unified metric computation and aggregation across multiple task types, whereas most benchmarks implement task-specific metrics without cross-task comparison infrastructure
evaluation result persistence and reporting
Medium confidencePersists evaluation results to structured formats (JSON, CSV, database) with full metadata (model, dataset, timestamp, parameters) enabling result reproducibility and historical tracking. The framework generates human-readable evaluation reports with visualizations (tables, charts), comparative analysis across models, and detailed breakdowns per task/category, supporting both programmatic result access and human-friendly reporting.
Implements structured result persistence with comprehensive metadata capture and flexible reporting generation, supporting both programmatic access and human-readable reports with comparative analysis across models and datasets
Provides integrated result persistence and reporting within the evaluation framework, whereas most benchmarks require separate tools for result storage and report generation
batch evaluation with parallelization and resource management
Medium confidenceOrchestrates batch evaluation of multiple models across multiple datasets with configurable parallelization (thread/process-based) and resource management (rate limiting, memory constraints, timeout handling). The framework distributes evaluation tasks across available resources, monitors execution, handles failures gracefully with retry logic, and provides progress tracking and resource utilization metrics.
Implements intelligent batch evaluation orchestration with configurable parallelization, automatic rate limiting, and failure handling, distributing evaluation tasks across available resources while respecting API constraints and resource limits
Provides built-in parallelization and resource management for batch evaluations, whereas most benchmarks require manual orchestration or external workflow tools
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓LLM researchers evaluating model capabilities across model families
- ✓Teams building math-heavy applications needing reliable performance baselines
- ✓Benchmark maintainers tracking LLM progress on reasoning tasks
- ✓Researchers studying LLM reasoning robustness and logical soundness
- ✓Teams building AI systems requiring formal verification or constraint satisfaction
- ✓Benchmark designers assessing reasoning quality beyond answer correctness
- ✓LLM researchers benchmarking code generation models (Codex, GPT-4, StarCoder, etc.)
- ✓Teams evaluating LLMs for code-generation-assisted development workflows
Known Limitations
- ⚠Exact match evaluation may penalize correct reasoning with minor formatting differences
- ⚠Numerical tolerance thresholds must be manually tuned per problem type
- ⚠Does not capture intermediate reasoning quality, only final answer correctness
- ⚠Limited to problems with deterministic numerical or symbolic answers
- ⚠Requires problems with formally verifiable solutions (not applicable to open-ended reasoning)
- ⚠Intermediate step validation depends on model explicitly stating reasoning (not guaranteed)
Requirements
Input / Output
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About
Unified evaluation framework for assessing LLMs on reasoning tasks without few-shot examples, covering mathematical reasoning, logical deduction, and code generation with standardized zero-shot evaluation protocols.
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