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| Pricing | Free | Free |
| Capabilities | 6 decomposed | 10 decomposed |
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Provides 113,000 question-answer pairs where each question requires traversing and reasoning across 2+ Wikipedia articles to derive the answer. The dataset includes explicit supporting fact annotations identifying which sentences from source documents are necessary for answering, enabling training of models that can both answer questions and explain their reasoning chains. Built through crowdsourced annotation with quality control mechanisms to ensure multi-hop reasoning is genuinely required rather than answerable from single documents.
Unique: Explicitly annotates supporting facts at sentence-level granularity rather than just providing QA pairs, enabling evaluation of both answer correctness AND reasoning transparency. The dataset design enforces multi-hop requirements through crowdsourcing validation that questions cannot be answered from single documents.
vs alternatives: Differs from SQuAD (single-document QA) and MS MARCO (web-scale but less structured) by providing explicit multi-hop reasoning requirements with supporting fact labels, making it uniquely suited for training interpretable reasoning systems rather than just answer extraction.
Provides a structured evaluation methodology for assessing whether QA systems can correctly identify which source sentences support their answers. The framework compares predicted supporting facts against human-annotated ground truth using precision, recall, and F1 metrics at both sentence and paragraph levels. This enables measurement of reasoning transparency independent of answer correctness, allowing diagnosis of whether a system found the right answer for the right reasons.
Unique: Decouples supporting fact evaluation from answer correctness, enabling independent assessment of reasoning transparency. Provides both sentence-level and paragraph-level metrics, allowing evaluation at different granularities depending on system architecture.
vs alternatives: Unlike generic QA metrics (EM/F1) that only measure answer correctness, this framework specifically evaluates whether systems can justify their reasoning, addressing the explainability gap in black-box QA systems.
Structures questions to require explicit composition of facts across multiple Wikipedia articles, creating a benchmark where naive single-document retrieval fails. Questions are designed such that the answer cannot be found in any single article; instead, the system must retrieve multiple relevant documents, identify the connecting entity or relationship, and synthesize information across them. This tests whether systems can perform true multi-hop reasoning versus pattern matching on single documents.
Unique: Explicitly validates that questions require multi-hop reasoning through crowdsourced verification that single-document retrieval cannot answer them. Questions are structured around entity linking and relationship composition, forcing systems to perform genuine multi-stage reasoning rather than single-stage retrieval.
vs alternatives: Compared to general QA datasets like Natural Questions (single-hop, web-scale) or SQuAD (single-document), HotpotQA's explicit multi-hop requirement and supporting fact annotations make it uniquely suited for evaluating whether systems perform compositional reasoning vs. pattern matching.
Provides a controlled evaluation setting where systems must distinguish relevant documents from distractors. The dataset includes both supporting documents (necessary for answering) and distractor documents (related to the question but not required for the answer). This tests whether retrieval systems can rank supporting documents above distractors, a critical capability for multi-hop QA where false positives in retrieval compound through reasoning stages. Evaluation measures whether systems retrieve all necessary documents while minimizing false positives.
Unique: Provides explicit distractor documents alongside supporting documents, enabling controlled evaluation of retrieval precision and recall. Distractors are selected to be topically related but not necessary for answering, testing whether systems can distinguish genuine supporting evidence from noise.
vs alternatives: Unlike open-domain QA datasets that evaluate retrieval against the full web, HotpotQA's controlled distractor set enables precise measurement of retrieval quality independent of corpus size, making it easier to diagnose retrieval failures in multi-hop systems.
Categorizes questions into distinct reasoning types (e.g., 'bridge' questions requiring entity linking between documents, 'comparison' questions requiring fact synthesis) and provides labels enabling analysis of system performance across reasoning patterns. This allows fine-grained evaluation of which reasoning types systems handle well vs. poorly, and enables targeted training or evaluation on specific compositional reasoning challenges. The taxonomy captures the structural reasoning requirements independent of domain content.
Unique: Provides explicit question type labels capturing the structural reasoning requirements (bridge, comparison, etc.) independent of domain content. Enables analysis of whether systems struggle with specific reasoning patterns vs. general knowledge gaps.
vs alternatives: Unlike generic QA datasets without reasoning type labels, HotpotQA's type taxonomy enables targeted evaluation and debugging of reasoning capabilities, allowing researchers to identify whether failures stem from retrieval, entity linking, or fact composition.
Questions are generated from Wikipedia articles and require reasoning over real-world entities, relationships, and facts. This grounds reasoning in a concrete knowledge domain (Wikipedia) rather than synthetic or template-based questions, enabling evaluation of whether systems can handle real-world complexity. Questions span diverse topics (people, places, films, organizations) and reasoning patterns (attribute lookup, entity linking, relationship chaining).
Unique: Questions are grounded in real Wikipedia entities and relationships rather than synthetic templates, requiring models to handle actual knowledge base complexity (entity disambiguation, relationship chaining, fact lookup). This makes reasoning evaluation more realistic than template-based datasets.
vs alternatives: Grounds reasoning in a real, large-scale knowledge base (Wikipedia) rather than synthetic examples, enabling evaluation of whether systems can handle real-world entity linking and relationship reasoning.
Aggregates 67 TB of source code from the Software Heritage archive, filtering for permissively licensed repositories (MIT, Apache 2.0, BSD, etc.) across 600+ programming languages. Uses automated license detection and validation to ensure legal compliance for model training. Implements a rigorous deduplication pipeline at file and repository levels to eliminate redundant training data and reduce dataset bloat.
Unique: Largest open-source code dataset at 67 TB with automated opt-out governance allowing repository owners to request removal, combined with rigorous deduplication and PII removal pipeline — no other public dataset offers this scale with legal compliance and community control mechanisms
vs alternatives: Larger and more legally compliant than GitHub's CodeSearchNet (14M files) or Google's BigQuery public datasets, with explicit opt-out governance vs. implicit inclusion, and covers 600+ languages vs. Codex training data's undisclosed language distribution
Implements a community-driven opt-out system where repository owners can request removal of their code from the dataset without legal takedown notices. Maintains a registry of excluded repositories and re-applies exclusions during dataset updates. Provides transparent governance documentation and a clear submission process for removal requests, balancing open access with creator rights.
Unique: First large-scale code dataset to implement opt-out governance at dataset level rather than relying solely on license compliance, with transparent registry and community submission process — shifts power from dataset creators to code contributors
vs alternatives: More respectful of creator autonomy than GitHub Copilot's training approach (no opt-out) or academic datasets (one-time snapshot), and more scalable than individual DMCA takedowns
The Stack v2 scores higher at 59/100 vs HotpotQA at 58/100.
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Automated pipeline that scans source code for personally identifiable information (email addresses, API keys, SSH keys, credit card patterns, phone numbers) and removes or redacts them before dataset release. Uses regex patterns, entropy-based detection for secrets, and heuristic rules to identify sensitive data. Operates at file level with configurable sensitivity thresholds to balance data utility against privacy risk.
Unique: Combines regex pattern matching, entropy-based secret detection, and heuristic rules in a unified pipeline with configurable sensitivity — more comprehensive than simple regex-only approaches, but trades off false positive rate against security coverage
vs alternatives: More thorough than GitHub's secret scanning (which only flags known patterns) because it includes entropy-based detection for unknown secret formats, but less accurate than specialized tools like TruffleHog due to language-agnostic approach
Indexes 67 TB of source code across 600+ programming languages with language-aware metadata (syntax, file extension, language family). Enables retrieval by language, license, repository, or code patterns. Uses Software Heritage's existing indexing infrastructure as foundation, augmented with language detection and classification. Supports both bulk download and filtered queries for specific language subsets.
Unique: Leverages Software Heritage's existing language detection and indexing infrastructure, then augments with BigCode-specific language classification and filtering — avoids reinventing language detection while providing dataset-specific query capabilities
vs alternatives: More comprehensive language coverage (600+ languages) than GitHub's Linguist (500+ languages) and more accessible than Software Heritage's raw API because it's pre-filtered for permissive licenses and deduplicated
Removes duplicate code files and repositories using content hashing (SHA-256 or similar) and fuzzy matching for near-duplicates. Operates in two stages: exact deduplication via hash matching, then fuzzy matching (e.g., Jaccard similarity or MinHash) to catch semantically identical code with minor formatting differences. Preserves one canonical copy of each unique code pattern while removing redundant training examples.
Unique: Two-stage deduplication combining exact hash matching with fuzzy similarity matching (likely MinHash or Jaccard) to catch both identical and near-identical code — more thorough than single-stage approaches but computationally expensive
vs alternatives: More aggressive deduplication than CodeSearchNet (which uses simple hash matching) because it catches near-duplicates, but less semantic than clone detection tools (which understand code structure) because it's content-based
Integrates with Software Heritage's comprehensive archive of 200+ million repositories and their full version control history. Extracts source code snapshots from Software Heritage's Git/Mercurial/SVN repositories, preserving repository metadata (commit history, author info, timestamps). Provides access to code at specific points in time, enabling historical analysis or training on code evolution patterns.
Unique: Leverages Software Heritage's universal code archive (200M+ repositories) as data source, providing access to code that would be impossible to collect via GitHub API alone — enables training on archived/deleted repositories and non-GitHub platforms (GitLab, Gitea, etc.)
vs alternatives: More comprehensive than GitHub-only datasets because it includes code from GitLab, Gitea, SourceForge, and other platforms archived by Software Heritage; more legally defensible than web scraping because it uses an established, community-maintained archive
Tracks and validates SPDX license identifiers for each repository, ensuring only permissively licensed code (MIT, Apache 2.0, BSD, etc.) is included. Maintains license metadata alongside code files, enabling downstream users to verify legal compliance. Implements license hierarchy and compatibility checking to handle dual-licensed or complex licensing scenarios.
Unique: Combines automated SPDX detection with manual review and maintains license metadata alongside code, enabling downstream users to verify compliance — more transparent than datasets that simply claim 'permissive licenses' without proof
vs alternatives: More legally rigorous than GitHub's CodeSearchNet (which doesn't validate licenses) and more transparent than Codex training data (which doesn't disclose license filtering at all)
Maintains versioned snapshots of the dataset (e.g., v2.0, v2.1) with documented changes between versions (new repositories added, deduplication improvements, PII removal updates). Provides checksums and manifests for reproducibility, enabling researchers to cite specific dataset versions and reproduce results. Tracks dataset lineage and transformation history.
Unique: Maintains semantic versioning and detailed changelogs for dataset releases, enabling researchers to cite specific versions and understand dataset evolution — more rigorous than one-off dataset releases without versioning
vs alternatives: More reproducible than academic datasets that are released once without versioning, and more transparent than commercial datasets (Codex) that don't disclose version history or changes
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