| Ecosystem | 0 | 0 |
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| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Evaluates QA systems on a two-stage pipeline: first retrieving relevant Wikipedia passages from 5.9M articles, then extracting answers from those passages. Unlike single-stage QA benchmarks, Natural Questions forces models to solve both information retrieval (finding the right document/passage) and reading comprehension (extracting the answer) in sequence, measuring end-to-end open-domain QA performance with 307,373 real Google Search queries paired with gold Wikipedia articles and human-annotated answers.
Unique: Uniquely combines information retrieval and reading comprehension evaluation in a single benchmark by requiring systems to first retrieve relevant passages from 5.9M Wikipedia articles, then extract answers — forcing end-to-end evaluation of both components rather than isolated QA on pre-selected passages like SQuAD
vs alternatives: More realistic than SQuAD (requires passage retrieval) and more scalable than MS MARCO (Wikipedia corpus is cleaner and more structured than web documents), making it the standard for evaluating production RAG systems
Dataset contains 307,373 naturally-occurring questions extracted from anonymized Google Search query logs, preserving the distribution and phrasing of actual user information needs rather than synthetic or crowdsourced questions. Questions span diverse topics, question types (factual, definitional, numerical), and difficulty levels, with natural language variation (typos, fragments, colloquialisms) that synthetic datasets cannot capture. This grounds evaluation in real user behavior and search intent patterns.
Unique: Sourced directly from anonymized Google Search logs rather than crowdsourced or synthetic generation, preserving natural question phrasing, ambiguity, and the actual distribution of user information needs at scale
vs alternatives: More representative of production search behavior than crowdsourced QA datasets (which exhibit annotation artifacts and unnatural phrasing), and more diverse than templated benchmarks
Each question is annotated with two complementary answer types: long answers (paragraph-level passages from Wikipedia, marked with start/end character offsets) and short answers (entity-level spans, marked with token indices). Annotators identify both levels from the same Wikipedia article, or mark the question as unanswerable if no answer exists. This dual annotation enables evaluation of both passage-level retrieval quality (can the system find the right paragraph?) and fine-grained answer extraction (can it identify the exact entity or phrase?).
Unique: Provides dual-level annotations (paragraph + entity) enabling independent evaluation of retrieval quality and extraction precision, rather than single-level annotations that conflate both stages
vs alternatives: More granular than SQuAD (which only provides short answer spans) and more realistic than synthetic QA pairs, allowing separate measurement of retrieval and extraction components
Annotators explicitly label each question as answerable or unanswerable based on whether a valid answer exists in the paired Wikipedia article. Unanswerable questions are not simply omitted — they are included in the benchmark with explicit labels, forcing QA systems to learn to recognize when no answer exists rather than always attempting extraction. This tests a critical capability for production systems: rejecting questions outside the knowledge base rather than hallucinating answers.
Unique: Explicitly includes unanswerable questions with labels rather than filtering them out, forcing systems to learn rejection as a valid output rather than always attempting answer extraction
vs alternatives: More realistic than QA benchmarks that only include answerable questions, and directly addresses the hallucination problem that production systems face
Benchmark includes the full 5.9M Wikipedia article corpus (2018 snapshot) as the retrieval target, requiring systems to rank relevant passages above irrelevant ones. Evaluation measures retrieval performance independently of answer extraction — systems are scored on whether they retrieve the correct Wikipedia article and passage before attempting to extract the answer. This decouples retrieval quality from extraction quality, enabling diagnosis of pipeline failures.
Unique: Provides a large-scale open-domain retrieval benchmark with 5.9M Wikipedia articles and real user queries, enabling evaluation of dense retrieval methods on realistic scale and diversity
vs alternatives: Larger and more realistic than MS MARCO (which uses web documents) and more structured than web-scale retrieval benchmarks, making it ideal for evaluating dense retrievers
Multiple annotators independently annotate each question with long and short answers, enabling measurement of inter-annotator agreement (IAA) and identification of ambiguous or difficult questions. Benchmark includes agreement metrics (e.g., F1 agreement between annotators) for each question, allowing researchers to filter by agreement level or analyze systematic disagreement patterns. This provides insight into question difficulty and annotation quality.
Unique: Includes explicit inter-annotator agreement metrics for each question, enabling researchers to understand benchmark reliability and filter by agreement level
vs alternatives: More transparent about annotation quality than benchmarks that hide disagreement, allowing researchers to make informed decisions about evaluation methodology
Benchmark enables computation of separate evaluation metrics for retrieval and extraction stages: retrieval metrics (recall@k, MRR) measure whether the correct Wikipedia article is ranked highly, while extraction metrics (F1, exact match) measure whether the answer span is correctly identified. Pipeline metrics (end-to-end F1) measure overall QA performance. This modular evaluation approach allows diagnosis of failures at each stage and comparison of different architectural choices.
Unique: Enables separate evaluation of retrieval and extraction stages, allowing researchers to measure stage-specific performance and diagnose pipeline bottlenecks
vs alternatives: More diagnostic than end-to-end QA metrics alone, and more realistic than isolated retrieval or extraction benchmarks
Natural Questions spans diverse Wikipedia article categories (science, history, biography, geography, etc.), enabling evaluation of QA system generalization across domains. Questions are paired with articles from different Wikipedia sections, testing whether systems can handle domain-specific terminology, article structures, and information patterns. This provides insight into cross-domain robustness beyond single-domain benchmarks.
Unique: Spans diverse Wikipedia domains and article types, enabling evaluation of cross-domain generalization rather than single-domain performance
vs alternatives: More diverse than domain-specific QA benchmarks, and more realistic than synthetic benchmarks that don't reflect real Wikipedia article distribution
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 Natural Questions at 60/100.
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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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