{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gsm8k","slug":"gsm8k","name":"GSM8K","type":"dataset","url":"https://github.com/openai/grade-school-math","page_url":"https://unfragile.ai/gsm8k","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gsm8k__cap_0","uri":"capability://data.processing.analysis.multi.step.mathematical.reasoning.benchmark.evaluation","name":"multi-step mathematical reasoning benchmark evaluation","description":"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.","intents":["Measure whether my language model can solve multi-step math word problems correctly","Compare reasoning capabilities across different model sizes and architectures","Identify failure modes in mathematical reasoning chains","Track improvement in reasoning ability across model iterations"],"best_for":["AI researchers evaluating LLM reasoning capabilities","Teams fine-tuning models for mathematical problem-solving","Benchmark maintainers tracking progress on standardized reasoning tasks"],"limitations":["Limited to grade school arithmetic (addition, subtraction, multiplication, division) — does not evaluate advanced mathematics like calculus or linear algebra","Test set is fixed at 1K examples, which may show saturation effects as models improve","Evaluation is binary (correct/incorrect final answer) — does not measure partial credit for correct intermediate steps","No evaluation of solution explanation quality or reasoning transparency, only final numeric correctness"],"requires":["Python 3.6+","JSON Lines format data files (train.jsonl, test.jsonl)","Model capable of generating text with #### answer delimiter format"],"input_types":["text (word problem statements)","structured JSON (problem-solution pairs)"],"output_types":["numeric accuracy metrics (percentage correct)","structured evaluation results (per-problem correctness)"],"categories":["data-processing-analysis","benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__cap_1","uri":"capability://code.generation.editing.calculator.integrated.solution.generation.with.annotation.based.computation","name":"calculator-integrated solution generation with annotation-based computation","description":"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.","intents":["Train models that learn to annotate intermediate calculations for transparency","Generate solutions where arithmetic is always correct, even if reasoning is flawed","Debug model reasoning by inspecting which calculations were performed","Improve solution quality by offloading arithmetic to a deterministic calculator"],"best_for":["Teams training models specifically for mathematical reasoning tasks","Researchers studying how models learn to decompose problems into calculable steps","Production systems requiring guaranteed arithmetic correctness in solutions"],"limitations":["Requires models to learn and consistently use the <<expression=result>> annotation format during training","Calculator only supports basic arithmetic operations (addition, subtraction, multiplication, division) — no support for functions, exponents, or complex expressions","Annotation format is rigid and may not generalize to models trained without this constraint","Inference-time calculator adds latency for expression parsing and evaluation per annotation"],"requires":["Python 3.6+","Training data with calculation annotations in <<expression=result>> format","Model fine-tuning pipeline that preserves annotation tokens during training"],"input_types":["text (problem statement and solution text with embedded annotations)"],"output_types":["text (solution with calculated results replacing annotation expressions)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__cap_2","uri":"capability://data.processing.analysis.socratic.format.guided.reasoning.dataset.with.subquestion.decomposition","name":"socratic-format guided reasoning dataset with subquestion decomposition","description":"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.","intents":["Train models that explicitly decompose problems into reasoning steps before solving","Evaluate whether guided reasoning improves solution accuracy","Generate more interpretable solutions with visible intermediate reasoning","Study how models learn to break down complex problems into simpler subproblems"],"best_for":["Researchers studying chain-of-thought reasoning and problem decomposition","Teams building interpretable AI systems where reasoning steps must be visible","Fine-tuning pipelines where guided reasoning improves downstream task performance"],"limitations":["Socratic subquestions are human-authored and may not generalize to problem domains outside grade school math","No automatic generation of subquestions — requires manual annotation for new problem types","Models must learn to follow the subquestion structure, which may constrain solution diversity","Evaluation still relies on final answer correctness, not quality of intermediate reasoning steps"],"requires":["Python 3.6+","Socratic format JSON Lines files with subquestion fields","Model architecture capable of processing multi-turn or multi-step prompts"],"input_types":["structured JSON (problem with embedded subquestions)"],"output_types":["text (step-by-step solution following subquestion guidance)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__cap_3","uri":"capability://data.processing.analysis.standardized.answer.extraction.and.correctness.comparison","name":"standardized answer extraction and correctness comparison","description":"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.","intents":["Automatically evaluate correctness of generated solutions at scale","Compare model performance across different problem subsets","Generate accuracy metrics for model selection and hyperparameter tuning","Identify which problem types or reasoning patterns cause failures"],"best_for":["ML engineers running large-scale model evaluations","Researchers comparing multiple model architectures on the same benchmark","Continuous evaluation pipelines that need reproducible, automated scoring"],"limitations":["Requires solutions to follow the #### answer format strictly — malformed answers are marked as incorrect","Only evaluates final answer correctness, not solution quality, reasoning clarity, or efficiency","No partial credit for nearly-correct answers (e.g., off-by-one errors are treated as fully incorrect)","Cannot detect if a correct answer was reached through flawed reasoning"],"requires":["Python 3.6+","Generated solutions with #### delimiter marking final answer","Ground truth answer values from dataset JSON"],"input_types":["text (generated solution with #### answer marker)","structured JSON (ground truth answer from dataset)"],"output_types":["boolean (correct/incorrect)","numeric (accuracy percentage across batch)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__cap_4","uri":"capability://data.processing.analysis.linguistically.diverse.problem.corpus.with.controlled.reasoning.complexity","name":"linguistically diverse problem corpus with controlled reasoning complexity","description":"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.","intents":["Benchmark models on problems that require genuine reasoning, not pattern matching","Measure robustness across linguistic variations of the same mathematical concept","Identify whether models solve problems through understanding or surface-level heuristics","Create a stable, non-saturating benchmark for long-term model evaluation"],"best_for":["Researchers validating that model improvements reflect genuine reasoning gains","Teams building production math-solving systems that must handle diverse problem phrasings","Benchmark maintainers seeking problems that resist gaming and memorization"],"limitations":["Limited to grade school arithmetic — does not include algebra, geometry, or advanced mathematics","Human authorship introduces potential biases in problem selection and phrasing","Fixed dataset of 8.5K problems may eventually saturate as models improve","No automatic generation of new problems — expanding the dataset requires manual authoring"],"requires":["Access to grade_school_math/data/ directory with .jsonl files","Python 3.6+ for loading and processing dataset"],"input_types":["structured JSON (problem-solution pairs with metadata)"],"output_types":["text (word problem statements)","text (step-by-step solutions)"],"categories":["data-processing-analysis","benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__cap_5","uri":"capability://data.processing.analysis.example.model.solutions.with.multi.size.performance.reference","name":"example model solutions with multi-size performance reference","description":"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.","intents":["Compare my model's performance against known baselines from different model sizes","Understand how model scale affects reasoning quality and solution approaches","Validate evaluation infrastructure by testing against reference solutions","Study qualitative differences in how different-sized models solve the same problem"],"best_for":["Researchers studying scaling laws in mathematical reasoning","Teams benchmarking new models and needing performance baselines","Evaluation engineers validating correctness of their evaluation pipelines"],"limitations":["Reference solutions are from specific models at specific training times — may not represent current SOTA","Limited to the model sizes included in the dataset — no solutions from custom or proprietary models","Solutions reflect the specific prompting and generation strategy used at creation time, which may not be optimal","No metadata about model training data, fine-tuning, or other factors that could affect performance"],"requires":["Python 3.6+","Access to example_model_solutions.jsonl file"],"input_types":["structured JSON (model solutions with metadata)"],"output_types":["text (reference solutions)","numeric (baseline accuracy metrics)"],"categories":["data-processing-analysis","benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__cap_6","uri":"capability://data.processing.analysis.json.lines.format.dataset.serialization.with.streaming.support","name":"json lines format dataset serialization with streaming support","description":"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.","intents":["Load large datasets efficiently without memory overhead","Process problems in streaming fashion for distributed training","Integrate dataset with PyTorch DataLoaders or TensorFlow tf.data pipelines","Append new problems to the dataset without rewriting entire files"],"best_for":["ML engineers building training pipelines with memory constraints","Teams using distributed training frameworks (PyTorch Lightning, Hugging Face Transformers)","Researchers who need to process datasets larger than available RAM"],"limitations":["JSON Lines format requires line-by-line parsing — random access to specific problems requires scanning from file start","No built-in compression — files are larger than binary formats like Protocol Buffers or MessagePack","Requires careful handling of malformed JSON lines — a single corrupted line can break parsing","No schema validation built-in — requires external validation to ensure all lines conform to expected structure"],"requires":["Python 3.6+ with json module","Disk space for 8.5K problems (approximately 50-100 MB uncompressed)"],"input_types":["JSON Lines text files (.jsonl)"],"output_types":["Python dictionaries (parsed JSON objects)","structured data (problem, solution, answer fields)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__cap_7","uri":"capability://automation.workflow.training.and.inference.pipeline.integration.with.model.sampling","name":"training and inference pipeline integration with model sampling","description":"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.","intents":["Fine-tune a language model on GSM8K problems end-to-end","Generate solutions from a trained model with controlled sampling behavior","Integrate calculator-based arithmetic into the generation pipeline","Evaluate model performance without writing custom evaluation code"],"best_for":["Researchers training custom models specifically for mathematical reasoning","Teams building math-solving systems that need integrated training and evaluation","ML engineers who want a complete pipeline without assembling components"],"limitations":["Pipeline is tightly coupled to GSM8K format — requires adaptation for other datasets","Sampling-based generation may produce variable quality solutions — requires multiple samples for reliable evaluation","No built-in support for advanced training techniques like reinforcement learning or curriculum learning","Inference pipeline assumes models can be loaded in memory — requires model quantization or sharding for very large models"],"requires":["Python 3.6+","PyTorch or TensorFlow for model training","Hugging Face Transformers library (or compatible model format)","GPU with sufficient VRAM for model fine-tuning (varies by model size)"],"input_types":["JSON Lines dataset files","pre-trained language model weights"],"output_types":["fine-tuned model weights","generated solutions (text)","evaluation metrics (accuracy)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gsm8k__headline","uri":"capability://data.processing.analysis.benchmark.dataset.for.evaluating.mathematical.reasoning.in.language.models","name":"benchmark dataset for evaluating mathematical reasoning in language models","description":"GSM8K is a benchmark dataset consisting of 8,500 grade school math word problems that require multi-step reasoning, designed to enhance the mathematical capabilities of language models.","intents":["best dataset for math reasoning","dataset for training language models on math problems","GSM8K for evaluating AI math skills","grade school math dataset for AI","math reasoning benchmark dataset"],"best_for":["researchers in AI","developers training language models"],"limitations":["limited to grade school level problems"],"requires":[],"input_types":["text-based math problems"],"output_types":["evaluated model responses"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"low","permissions":["Python 3.6+","JSON Lines format data files (train.jsonl, test.jsonl)","Model capable of generating text with #### answer delimiter format","Training data with calculation annotations in <<expression=result>> format","Model fine-tuning pipeline that preserves annotation tokens during training","Socratic format JSON Lines files with subquestion fields","Model architecture capable of processing multi-turn or multi-step prompts","Generated solutions with #### delimiter marking final answer","Ground truth answer values from dataset JSON","Access to grade_school_math/data/ directory with .jsonl files"],"failure_modes":["Limited to grade school arithmetic (addition, subtraction, multiplication, division) — does not evaluate advanced mathematics like calculus or linear algebra","Test set is fixed at 1K examples, which may show saturation effects as models improve","Evaluation is binary (correct/incorrect final answer) — does not measure partial credit for correct intermediate steps","No evaluation of solution explanation quality or reasoning transparency, only final numeric correctness","Requires models to learn and consistently use the <<expression=result>> annotation format during training","Calculator only supports basic arithmetic operations (addition, subtraction, multiplication, division) — no support for functions, exponents, or complex expressions","Annotation format is rigid and may not generalize to models trained without this constraint","Inference-time calculator adds latency for expression parsing and evaluation per annotation","Socratic subquestions are human-authored and may not generalize to problem domains outside grade school math","No automatic generation of subquestions — requires manual annotation for new problem types","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=gsm8k","compare_url":"https://unfragile.ai/compare?artifact=gsm8k"}},"signature":"32Rhc6i7drTg/6WHWk97UNRcCvyuwSPaZFzkEHuC4GVCZ0RTMVwbk6Pv/JXRhc1ABctomVxHnnvv5ugEdfx9Dw==","signedAt":"2026-06-20T22:39:52.939Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gsm8k","artifact":"https://unfragile.ai/gsm8k","verify":"https://unfragile.ai/api/v1/verify?slug=gsm8k","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}