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| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Evaluates language models' ability to perform 2-8 step mathematical reasoning on grade school word problems through a curated dataset of 8,500 problems split into 7.5K training and 1K test examples. The evaluation framework extracts final answers marked with #### delimiters and compares them against ground truth, enabling precise measurement of multi-step reasoning accuracy across model architectures and sizes.
Unique: Uses linguistically diverse, human-authored grade school problems (not synthetic) that require genuine multi-step reasoning with basic arithmetic, combined with a standardized answer extraction format (#### delimiter) that enables reproducible evaluation across heterogeneous model outputs
vs alternatives: More challenging than simple arithmetic benchmarks (requires 2-8 reasoning steps) yet more accessible than advanced math benchmarks, making it ideal for measuring practical reasoning improvements in production models
Enables language models to generate mathematically correct solutions by embedding calculation annotations in the format <<expression=result>> within generated text. During training, models learn these annotations as normal tokens; during inference, a calculator system detects expressions between << and >> delimiters, evaluates them accurately, and replaces them with computed results, preventing arithmetic errors in multi-step chains.
Unique: Dual-mode annotation system where the same <<expression=result>> format serves as training signal (models learn to produce it) and inference hook (calculator detects and evaluates it), creating a learnable interface between language generation and deterministic computation without requiring separate tool-calling infrastructure
vs alternatives: Simpler than external tool-calling APIs (no function registry or schema negotiation needed) and more interpretable than black-box arithmetic, but less flexible than full function-calling systems for complex operations
Provides an alternative dataset format (train_socratic.jsonl, test_socratic.jsonl) where each problem is augmented with intermediate Socratic subquestions that guide step-by-step reasoning. This format enables training models to decompose problems into smaller reasoning steps before solving, improving interpretability and potentially reducing errors in multi-step chains by enforcing explicit intermediate reasoning.
Unique: Augments standard problems with human-authored Socratic subquestions that decompose reasoning into explicit intermediate steps, creating a structured reasoning scaffold that models can learn from without requiring external prompting or chain-of-thought engineering
vs alternatives: More structured than zero-shot chain-of-thought prompting (reasoning steps are baked into training data) but less flexible than dynamic prompting systems that generate subquestions at inference time
Implements a deterministic answer extraction pipeline that parses generated solutions to locate the final answer marked with #### delimiter, extracts the numeric value, and compares it against ground truth answers from the dataset. This enables automated evaluation of solution correctness without manual inspection, supporting batch evaluation across thousands of model outputs with consistent, reproducible metrics.
Unique: Uses a simple, language-agnostic delimiter format (####) for answer marking that works across any model output format, combined with numeric comparison logic that handles floating-point precision and integer equivalence, enabling consistent evaluation without model-specific parsing
vs alternatives: More robust than regex-based answer extraction (explicit delimiter is unambiguous) and more scalable than manual evaluation, but less sophisticated than semantic similarity metrics that could credit partially correct reasoning
Curates 8,500 human-authored grade school math word problems with explicit control over reasoning complexity (2-8 steps per problem) and linguistic diversity to prevent models from exploiting surface-level patterns. The dataset balances problem difficulty, operation types, and linguistic variation to create a robust benchmark that measures genuine mathematical reasoning rather than pattern matching or memorization.
Unique: Human-authored problems with explicit step-count constraints (2-8 steps) and linguistic diversity ensure that models cannot solve problems through surface-level pattern matching or memorization, forcing evaluation of genuine multi-step reasoning capability
vs alternatives: More challenging than synthetic or template-based benchmarks (human authorship prevents exploitable patterns) and more stable than crowdsourced datasets (controlled authorship ensures consistency), but smaller than web-scraped math problem collections
Provides pre-generated solutions from models of varying sizes (available in example_model_solutions.jsonl) that serve as reference implementations and performance baselines. These solutions demonstrate how different model scales approach the same problems, enabling researchers to study scaling laws in mathematical reasoning and to validate evaluation infrastructure against known model outputs.
Unique: Pre-computed solutions from multiple model sizes in a single standardized file enable direct comparison of how model scale affects reasoning quality without requiring researchers to re-run inference on large models, reducing computational overhead for benchmarking studies
vs alternatives: More convenient than running inference on reference models yourself (no compute cost) but less flexible than dynamic baselines that could be updated as new models emerge
Stores all problems and solutions in JSON Lines format (.jsonl), where each line is a complete, self-contained JSON object representing one problem-solution pair. This format enables efficient streaming loading of large datasets without loading entire files into memory, supports line-by-line processing in data pipelines, and allows easy integration with distributed training frameworks that process data in batches.
Unique: Uses line-delimited JSON format that enables streaming processing without loading entire dataset into memory, combined with self-contained problem-solution pairs that allow independent processing of each example in distributed training pipelines
vs alternatives: More memory-efficient than monolithic JSON files and more human-readable than binary formats, but slower for random access than indexed databases or columnar formats like Parquet
Provides infrastructure for training models on GSM8K data and generating solutions through sampling-based inference. The pipeline handles data loading, model fine-tuning, solution generation with temperature/sampling parameters, and integration with the calculator system to ensure arithmetic correctness. This enables end-to-end workflows from raw dataset to evaluated model performance without external tooling.
Unique: Integrates dataset loading, model training, solution generation, calculator evaluation, and answer extraction into a single end-to-end pipeline, with sampling-based inference that allows temperature control for exploring solution diversity while maintaining arithmetic correctness through calculator integration
vs alternatives: More complete than standalone dataset (includes training and inference code) but less flexible than modular frameworks that allow swapping components; tightly integrated for GSM8K but requires customization for other tasks
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 GSM8K at 58/100.
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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.
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