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| Pricing | Free | Free |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Evaluates multimodal models' ability to understand spatial relationships, object positioning, and geometric reasoning within real-world photographic scenes. The benchmark presents images with questions requiring models to reason about relative positions, distances, containment, and spatial arrangements without relying on synthetic or controlled environments, forcing models to handle natural occlusion, perspective distortion, and complex scene layouts.
Unique: Uses uncontrolled real-world photographs instead of synthetic scenes or curated datasets, forcing models to handle natural visual complexity including occlusion, perspective distortion, and lighting variation — architectural choice that prioritizes practical deployment scenarios over controlled evaluation conditions
vs alternatives: More representative of real-world VLM deployment challenges than synthetic spatial reasoning benchmarks like GQA or CLEVR, but introduces confounding variables that make error attribution harder than controlled alternatives
Benchmarks multimodal models' ability to accurately count objects in real-world photographs, including handling of partial occlusion, dense clusters, and varying object scales. The evaluation presents images where models must enumerate instances of specific object categories without access to bounding boxes or segmentation masks, requiring robust visual attention and numerical reasoning on naturally-occurring scenes.
Unique: Evaluates counting on real-world photographs with natural occlusion and scale variation rather than synthetic scenes with uniform object appearance, requiring models to handle visual ambiguity and partial visibility — architectural choice that tests practical robustness over controlled accuracy
vs alternatives: More realistic than synthetic counting benchmarks but lacks the fine-grained error analysis and object definition consistency of controlled datasets like COCO-Count
Evaluates multimodal models' ability to read, recognize, and extract text visible in real-world photographs including signage, labels, documents, and handwritten text. The benchmark tests OCR-like capabilities integrated into vision-language models, requiring models to handle variable text orientation, fonts, lighting conditions, and partial occlusion without explicit OCR preprocessing, assessing end-to-end text understanding in natural scenes.
Unique: Tests integrated text reading within vision-language models on real-world photographs rather than synthetic text or isolated OCR tasks, requiring models to handle natural text variation (orientation, fonts, lighting, occlusion) without preprocessing — architectural choice that evaluates practical end-to-end text understanding
vs alternatives: More representative of real-world VLM text understanding than synthetic OCR benchmarks, but less controlled than dedicated OCR datasets like ICDAR which provide character-level annotations
Evaluates multimodal models' ability to apply world knowledge and common-sense reasoning to answer questions about real-world photographs that require understanding of object affordances, social conventions, physical laws, and practical reasoning. The benchmark presents images where correct answers depend on implicit knowledge about how the world works rather than explicit visual features, testing whether models have internalized practical understanding during pretraining.
Unique: Evaluates common-sense reasoning on real-world photographs where correct answers require implicit world knowledge rather than explicit visual features, testing whether models have internalized practical understanding during pretraining — architectural choice that assesses reasoning capability beyond visual pattern matching
vs alternatives: More representative of real-world reasoning requirements than visual-only benchmarks, but harder to validate and more prone to annotation bias than benchmarks with objective ground truth
Provides a standardized benchmark dataset and evaluation protocol for comparing vision-language models on a diverse set of real-world visual understanding tasks. The framework enables researchers to load the dataset via HuggingFace, run their models against consistent test cases, and generate comparable metrics across spatial reasoning, counting, text reading, and common-sense tasks, facilitating reproducible evaluation and model comparison.
Unique: Provides a unified benchmark combining multiple visual understanding tasks (spatial reasoning, counting, text reading, common-sense) on real-world photographs rather than separate task-specific benchmarks, enabling holistic VLM evaluation — architectural choice that tests practical multimodal capabilities in integrated fashion
vs alternatives: More comprehensive than single-task benchmarks like VQA or COCO-Captions, but less specialized than task-specific benchmarks which may provide deeper error analysis
Curates and annotates a collection of real-world photographs with diverse visual understanding tasks (spatial reasoning, counting, text reading, common-sense questions) rather than using synthetic or controlled images. The curation process selects images that require practical visual understanding without relying on dataset-specific artifacts, and annotations include question-answer pairs that test genuine multimodal reasoning rather than superficial pattern matching.
Unique: Curates real-world photographs with diverse visual understanding annotations rather than using synthetic scenes or existing image datasets, prioritizing practical visual complexity and natural variation — architectural choice that ensures benchmark reflects real-world deployment scenarios
vs alternatives: More representative of real-world VLM deployment than synthetic benchmarks like CLEVR, but introduces annotation consistency challenges and confounding variables compared to controlled datasets
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 RealWorldQA 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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