| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Generates 58K multi-turn dialogue examples where GPT-4V analyzes images and engages in extended conversations about visual content. The dataset captures sequential question-answer pairs with context preservation across turns, enabling models to maintain coherent visual reasoning across multiple exchanges. This approach uses GPT-4V's vision capabilities to ground conversations in actual image content rather than synthetic descriptions.
Unique: Uses GPT-4V to generate conversations that maintain visual context across multiple turns, rather than generating isolated image-text pairs. The dataset preserves dialogue coherence and reference resolution across sequential exchanges, enabling training of models that understand conversation flow in visual contexts.
vs alternatives: Captures multi-turn visual reasoning patterns that single-turn datasets (like COCO Captions) cannot represent, producing models better suited for conversational visual AI applications than datasets generated from language-only models.
Generates 23K comprehensive image descriptions using GPT-4V that go beyond simple captions to include spatial relationships, object attributes, scene context, and visual details. Each description is structured to capture fine-grained visual information that enables models to understand complex visual scenes. The generation leverages GPT-4V's ability to produce detailed natural language descriptions grounded in actual image content.
Unique: Generates descriptions at semantic depth beyond typical captions, including spatial relationships, object attributes, and scene composition. Uses GPT-4V's multimodal understanding to produce descriptions that capture visual nuance rather than surface-level object lists.
vs alternatives: Produces richer training signal than automated caption datasets (COCO, Flickr30K) because GPT-4V understands visual semantics; stronger than human-annotated datasets at scale due to consistency and coverage, though potentially less diverse than crowdsourced descriptions.
Generates 77K instruction-following examples that require multi-step visual reasoning, including counting, spatial reasoning, attribute comparison, and scene understanding. Each example pairs an image with a complex question and detailed answer generated by GPT-4V. The dataset is structured to train models on reasoning patterns that go beyond simple visual recognition, incorporating logical inference over visual elements.
Unique: Largest component (77K examples) focused specifically on reasoning tasks rather than simple recognition. Uses GPT-4V to generate questions that require multi-step inference, spatial understanding, and logical reasoning over visual elements, creating a reasoning-focused instruction tuning signal.
vs alternatives: Larger and more reasoning-focused than existing VQA datasets (GQA, OK-VQA) because it leverages GPT-4V's ability to generate diverse reasoning questions at scale; stronger training signal for reasoning than datasets with simple factual questions.
Provides a dataset specifically designed to align pre-trained vision encoders with language models through instruction-following examples. The dataset demonstrates that a frozen vision encoder (e.g., CLIP) can be effectively aligned with a language model using only instruction-tuning data, without requiring end-to-end vision-language pre-training. This approach uses GPT-4V-generated examples to create a bridge between independent vision and language components.
Unique: Demonstrates that instruction tuning with GPT-4V-generated examples can effectively align independent vision and language components without end-to-end pre-training. The dataset is specifically structured to bridge the modality gap through instruction-following rather than contrastive or generative pre-training objectives.
vs alternatives: More efficient than end-to-end vision-language pre-training (BLIP, ALBEF) because it reuses frozen encoders; more practical than datasets requiring human annotation at scale; stronger alignment signal than generic image-text pairs because examples are instruction-grounded.
Leverages GPT-4V's multimodal understanding to generate consistent, high-quality instruction-following examples with implicit quality control. Each example is generated by GPT-4V analyzing the actual image, ensuring descriptions and answers are grounded in visual content rather than hallucinated. This approach uses GPT-4V as both a data generator and implicit quality filter, producing dataset examples where text is verifiable against image content.
Unique: Uses GPT-4V's multimodal understanding as an implicit quality control mechanism; each example is generated by analyzing the actual image, ensuring text is grounded in visual content. This approach eliminates hallucinated examples where text describes content not present in images.
vs alternatives: Higher implicit quality than crowdsourced datasets (COCO, Flickr) because GPT-4V verifies text-image alignment; more consistent than human-annotated datasets due to GPT-4V's deterministic generation; more scalable than manual quality review but potentially less diverse than human-generated examples.
Provides a unified dataset combining three distinct task types (conversations, descriptions, reasoning) into a single instruction-following corpus. The dataset is structured to train models on diverse visual understanding tasks simultaneously, with 150K total examples spanning different reasoning patterns and interaction modalities. This multi-task structure enables models to learn generalizable visual understanding capabilities rather than task-specific patterns.
Unique: Combines three distinct task types (conversations, descriptions, reasoning) into a unified 150K-example corpus rather than separate task-specific datasets. The multi-task structure enables models to learn generalizable visual understanding patterns that transfer across different interaction modalities and reasoning requirements.
vs alternatives: More comprehensive than single-task datasets (COCO Captions for descriptions, GQA for reasoning) because it covers multiple visual understanding patterns; enables better generalization than task-specific training because models learn shared visual representations across diverse tasks.
Provides 150K instruction-following examples at scale, enabling training of multimodal models with sufficient data diversity and volume to learn robust visual understanding. The dataset size and diversity allow models to learn generalizable patterns rather than memorizing specific examples. This scale is achieved through systematic GPT-4V-based generation rather than manual annotation, making large-scale dataset creation feasible.
Unique: Achieves 150K-example scale through systematic GPT-4V-based generation rather than manual annotation, making large-scale instruction tuning datasets feasible. The scale enables training of models with sufficient data diversity to learn generalizable visual understanding patterns.
vs alternatives: Larger than most manually-annotated visual instruction datasets (COCO is 330K images but fewer instruction examples); more cost-effective than human annotation at scale; enables training of models competitive with larger proprietary datasets through efficient generation.
Structures all 150K examples as instruction-response pairs in a format compatible with supervised fine-tuning (SFT) pipelines. Each example pairs a visual instruction (question, task, or directive) with a corresponding response grounded in image content. The format supports standard SFT loss computation where models learn to predict responses given instructions and images. This standardization enables direct integration with existing fine-tuning frameworks and training recipes.
Unique: Standardizes all data into instruction-response pairs compatible with SFT pipelines, enabling direct integration with existing training frameworks without custom data processing. This removes friction from training while maintaining compatibility with standard loss functions and optimization procedures.
vs alternatives: More immediately usable than raw image-text pairs because it provides pre-structured instructions and responses. More flexible than domain-specific formats because it works with any SFT framework supporting image-text inputs.
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 LLaVA-Instruct 150K at 59/100.
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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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