{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"triviaqa","slug":"triviaqa","name":"TriviaQA","type":"dataset","url":"https://huggingface.co/datasets/trivia_qa","page_url":"https://unfragile.ai/triviaqa","categories":["model-training","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"triviaqa__cap_0","uri":"capability://data.processing.analysis.open.domain.question.answer.pair.dataset.with.evidence.documents","name":"open-domain question-answer pair dataset with evidence documents","description":"Provides 95,000 human-authored trivia questions paired with multiple Wikipedia and web-sourced evidence documents that require cross-document reasoning to answer. The dataset architecture includes question text, answer strings, and a collection of retrieved documents ranked by relevance, enabling training and evaluation of retrieval-augmented QA systems that must synthesize information across noisy, real-world sources rather than relying on single curated contexts.","intents":["Train open-domain QA models that can retrieve and reason over multiple evidence sources","Evaluate retrieval-augmented generation systems on their ability to find supporting evidence and synthesize answers","Benchmark question answering performance on questions requiring world knowledge beyond simple text matching","Develop and test cross-document reasoning capabilities in language models"],"best_for":["Researchers building retrieval-augmented QA systems","Teams evaluating open-domain question answering models","ML engineers training dense passage retrievers and reader models","Organizations benchmarking RAG pipeline performance"],"limitations":["Questions authored by trivia enthusiasts may have inherent biases toward certain knowledge domains (sports, entertainment, history)","Evidence documents sourced from Wikipedia and web crawls contain noise, contradictions, and outdated information that mirrors real-world retrieval challenges","Answer strings may be incomplete or ambiguous — some questions have multiple valid phrasings or partial answers","No explicit annotation of which documents are necessary vs. sufficient for answering, requiring models to learn relevance implicitly","Dataset is English-only with no multilingual variants"],"requires":["Hugging Face datasets library (transformers>=4.0)","Python 3.7+","Sufficient disk space (~2-3 GB for full dataset with evidence documents)","Internet connection for initial download and caching"],"input_types":["question text (string)","candidate evidence documents (list of text passages)"],"output_types":["answer string (text)","document relevance scores (float)","supporting document indices (integer)"],"categories":["data-processing-analysis","memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"triviaqa__cap_1","uri":"capability://search.retrieval.multi.document.evidence.retrieval.and.ranking.evaluation","name":"multi-document evidence retrieval and ranking evaluation","description":"Enables evaluation of retrieval systems by providing ground-truth document relevance labels — each question includes multiple evidence documents ranked by their utility for answering. The dataset structure supports computing retrieval metrics (recall@k, MRR, NDCG) and measuring whether retrievers can identify supporting documents from large corpora, with separate Wikipedia and web evidence tracks allowing evaluation of retrieval quality across different source distributions.","intents":["Measure retrieval recall and ranking quality of dense passage retrievers against ground-truth supporting documents","Evaluate whether retrieval systems can find relevant evidence from Wikipedia vs. web sources","Benchmark end-to-end retrieval-reader pipeline performance on open-domain QA","Identify failure modes in retrieval (missing documents, low ranking of relevant passages)"],"best_for":["Information retrieval researchers optimizing dense retrievers","Teams building production RAG systems who need realistic evaluation","ML engineers tuning retrieval hyperparameters (embedding models, ranking functions)","Researchers studying cross-document reasoning in QA"],"limitations":["Ground-truth relevance is binary (document is supporting or not) rather than graded, limiting fine-grained ranking evaluation","Evidence documents are pre-retrieved and provided; the dataset does not include the full corpus for open-retrieval evaluation without external corpus setup","Relevance judgments reflect Wikipedia/web document availability at dataset creation time; newer or updated documents are not included","No explicit annotation of document necessity (some questions may be answerable from subset of provided documents)"],"requires":["Hugging Face datasets library","Python 3.7+","External retrieval corpus (Wikipedia dump or web index) for full open-retrieval evaluation","Evaluation metrics library (e.g., pytrec_eval for NDCG/MRR computation)"],"input_types":["question text (string)","candidate document passages (list of text)","retriever output rankings (list of document indices and scores)"],"output_types":["retrieval metrics (recall@k, MRR, NDCG as float)","document relevance labels (binary or graded)","ranking quality scores (float)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"triviaqa__cap_2","uri":"capability://planning.reasoning.cross.document.reasoning.and.synthesis.evaluation","name":"cross-document reasoning and synthesis evaluation","description":"Provides a benchmark for evaluating models' ability to synthesize answers from multiple documents that collectively contain the answer but may require reasoning across sources. Questions are authored to require integration of information from different documents (e.g., combining facts from multiple Wikipedia articles), and the dataset structure includes multiple evidence documents per question, enabling evaluation of whether models can identify relevant documents and reason across them rather than matching single passages.","intents":["Evaluate whether QA models can synthesize information across multiple documents rather than relying on single-passage matching","Test multi-hop reasoning capabilities where the answer requires combining facts from different sources","Benchmark retrieval-reader systems on their ability to identify and integrate relevant documents","Measure performance degradation when evidence documents are noisy or contradictory"],"best_for":["Researchers studying multi-hop reasoning and cross-document understanding","Teams building advanced RAG systems that require document synthesis","ML engineers evaluating whether models perform true reasoning vs. surface-level matching","Organizations testing robustness to noisy or contradictory evidence"],"limitations":["No explicit annotation of which documents are necessary for answering vs. which are red herrings, requiring implicit learning","Questions may be answerable from single documents despite being authored to require multiple sources","No structured reasoning chains or intermediate steps provided — only questions, answers, and documents","Reasoning complexity is not quantified; some questions may require simple concatenation while others need true synthesis","Web evidence documents may be outdated or contain contradictory information, making ground truth ambiguous"],"requires":["Hugging Face datasets library","Python 3.7+","Language model capable of multi-document reasoning (e.g., T5, BART, or LLM with RAG)","Evaluation framework for measuring reasoning quality (e.g., BLEU, ROUGE, or human evaluation)"],"input_types":["question text (string)","multiple evidence documents (list of text passages)","document relevance labels (binary)"],"output_types":["synthesized answer text (string)","document selection/ranking (list of indices)","reasoning quality metrics (float)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"triviaqa__cap_3","uri":"capability://data.processing.analysis.world.knowledge.and.domain.coverage.evaluation","name":"world knowledge and domain coverage evaluation","description":"Provides a diverse benchmark spanning multiple knowledge domains (history, science, sports, entertainment, geography, etc.) authored by trivia enthusiasts, enabling evaluation of whether models possess broad world knowledge beyond specific domains. The dataset's scale (95,000 questions) and diversity allow measurement of model performance across knowledge categories and identification of domain-specific weaknesses in retrieval and reasoning.","intents":["Measure whether QA models have broad world knowledge across multiple domains","Identify domain-specific performance gaps (e.g., poor performance on science vs. sports questions)","Evaluate knowledge coverage of retrieval corpora (Wikipedia vs. web sources)","Benchmark general-purpose QA systems against domain-specific baselines"],"best_for":["Researchers studying knowledge representation and coverage in language models","Teams building general-purpose QA systems that must handle diverse domains","ML engineers evaluating whether models have balanced knowledge across categories","Organizations assessing knowledge gaps in their retrieval corpora"],"limitations":["Domain distribution reflects trivia enthusiast interests, which may skew toward entertainment, sports, and history over technical or scientific domains","No explicit domain labels provided in the dataset; domain categorization requires external annotation or question text analysis","Questions authored by enthusiasts may have cultural biases (e.g., Western-centric knowledge)","Knowledge required is static (dataset creation time); emerging or recent knowledge is not included","No quantification of question difficulty or knowledge specificity"],"requires":["Hugging Face datasets library","Python 3.7+","Optional: domain classification model or manual annotation for category-level analysis","Knowledge base or retrieval corpus covering diverse domains"],"input_types":["question text (string)","domain category (string, if manually annotated)"],"output_types":["answer text (string)","domain-level performance metrics (float)","knowledge coverage analysis (structured data)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"triviaqa__cap_4","uri":"capability://safety.moderation.noisy.real.world.evidence.handling.and.robustness.evaluation","name":"noisy real-world evidence handling and robustness evaluation","description":"Includes evidence documents sourced from actual Wikipedia and web crawls (not curated or cleaned), enabling evaluation of how QA systems handle noisy, contradictory, or irrelevant information. The dataset structure provides multiple documents per question, some of which may contain conflicting information or be only tangentially relevant, allowing measurement of model robustness to real-world retrieval noise and evaluation of whether systems can filter irrelevant evidence.","intents":["Evaluate QA system robustness to noisy, contradictory, or irrelevant evidence documents","Measure whether models can distinguish relevant from irrelevant documents in realistic retrieval scenarios","Test handling of conflicting information across documents","Benchmark production RAG systems on real-world evidence quality challenges"],"best_for":["Teams building production RAG systems that must handle real-world retrieval noise","Researchers studying robustness and failure modes in QA systems","ML engineers evaluating evidence filtering and ranking strategies","Organizations assessing RAG system reliability on noisy corpora"],"limitations":["No explicit annotation of document quality, noise level, or relevance confidence; noise is implicit in real-world sources","Contradictory information across documents is not labeled or quantified","No ground truth for which documents are 'correct' when conflicts exist","Web evidence may be outdated or removed, making dataset reproduction difficult","Noise characteristics reflect Wikipedia and web quality at dataset creation time; modern sources may differ"],"requires":["Hugging Face datasets library","Python 3.7+","QA system capable of handling multiple documents (retrieval-reader or RAG architecture)","Optional: document quality assessment model or manual evaluation framework"],"input_types":["question text (string)","multiple evidence documents with varying quality (list of text)","document relevance labels (binary)"],"output_types":["answer text (string)","document filtering/ranking decisions (list of indices and scores)","robustness metrics (accuracy under noise, F1 on document selection)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"triviaqa__cap_5","uri":"capability://code.generation.editing.answer.span.extraction.and.evaluation.metrics.for.reading.comprehension","name":"answer span extraction and evaluation metrics for reading comprehension","description":"Provides ground-truth answer spans within evidence documents, enabling training and evaluation of reading comprehension models that extract answers from retrieved passages. The dataset includes multiple valid answer spans per question (accounting for paraphrasing and synonymy), allowing evaluation metrics like Exact Match (EM) and F1 score that measure token-level overlap. The span annotations enable training of span-based QA models (e.g., BERT-based extractive QA) and evaluation of their ability to locate and extract answer text from noisy documents.","intents":["Train extractive QA models that locate and extract answer spans from retrieved documents","Evaluate reading comprehension models using EM and F1 metrics on held-out test questions","Develop span-based answer extraction pipelines that identify answer boundaries in retrieved passages","Analyze reading comprehension performance across question types and document lengths"],"best_for":["ML engineers training extractive QA models (BERT, RoBERTa, ELECTRA) for production systems","Researchers studying reading comprehension and span extraction in noisy documents","Teams building end-to-end QA pipelines combining retrieval and reading comprehension","Organizations evaluating trade-offs between extractive and generative QA approaches"],"limitations":["Answer spans are limited to text present in documents; cannot evaluate generative QA models that paraphrase answers","Multiple valid answer spans per question require careful handling during training (e.g., using max loss across spans)","Span annotations may not cover all valid answer phrasings, leading to false negatives in evaluation","F1 and EM metrics are token-level and may not capture semantic correctness of answers","Extractive approach fails for questions requiring reasoning or synthesis beyond span selection"],"requires":["Reading comprehension model (BERT, RoBERTa, ELECTRA, or custom transformer-based model)","PyTorch or TensorFlow for training span extraction models","Evaluation framework supporting EM and F1 metrics (e.g., SQuAD evaluation script)","Python 3.7+ with Transformers library (transformers>=4.0.0)"],"input_types":["question (text)","evidence document (text passage)","answer span (character offsets or text)"],"output_types":["predicted answer span (character offsets or text)","exact match (EM) score (binary: correct/incorrect)","F1 score (token-level overlap with gold answer)","span extraction confidence scores"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"triviaqa__headline","uri":"capability://data.processing.analysis.open.domain.question.answering.dataset","name":"open-domain question answering dataset","description":"TriviaQA is a large-scale dataset designed for open-domain question answering, featuring 95,000 trivia questions paired with supporting documents from Wikipedia and the web, requiring complex reasoning and synthesis of information.","intents":["best open-domain QA dataset","open-domain QA dataset for training","top datasets for question answering","datasets for trivia question generation","QA evaluation datasets comparison"],"best_for":["research in question answering","training QA models"],"limitations":["requires substantial computational resources for training"],"requires":["familiarity with machine learning frameworks"],"input_types":["text questions"],"output_types":["text answers"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Hugging Face datasets library (transformers>=4.0)","Python 3.7+","Sufficient disk space (~2-3 GB for full dataset with evidence documents)","Internet connection for initial download and caching","Hugging Face datasets library","External retrieval corpus (Wikipedia dump or web index) for full open-retrieval evaluation","Evaluation metrics library (e.g., pytrec_eval for NDCG/MRR computation)","Language model capable of multi-document reasoning (e.g., T5, BART, or LLM with RAG)","Evaluation framework for measuring reasoning quality (e.g., BLEU, ROUGE, or human evaluation)","Optional: domain classification model or manual annotation for category-level analysis"],"failure_modes":["Questions authored by trivia enthusiasts may have inherent biases toward certain knowledge domains (sports, entertainment, history)","Evidence documents sourced from Wikipedia and web crawls contain noise, contradictions, and outdated information that mirrors real-world retrieval challenges","Answer strings may be incomplete or ambiguous — some questions have multiple valid phrasings or partial answers","No explicit annotation of which documents are necessary vs. sufficient for answering, requiring models to learn relevance implicitly","Dataset is English-only with no multilingual variants","Ground-truth relevance is binary (document is supporting or not) rather than graded, limiting fine-grained ranking evaluation","Evidence documents are pre-retrieved and provided; the dataset does not include the full corpus for open-retrieval evaluation without external corpus setup","Relevance judgments reflect Wikipedia/web document availability at dataset creation time; newer or updated documents are not included","No explicit annotation of document necessity (some questions may be answerable from subset of provided documents)","No explicit annotation of which documents are necessary for answering vs. which are red herrings, requiring implicit learning","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":0.75,"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":"2026-05-24T12:16:34.118Z","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=triviaqa","compare_url":"https://unfragile.ai/compare?artifact=triviaqa"}},"signature":"mhJWmAzbsUEIAcwenEsxfPb+sIUrvpS6SEzE7v05L3Y+pmDSQ7Tba3iCZHqNygHdxBa5qVng2dgqL1pC5bTcCA==","signedAt":"2026-06-23T13:11:12.396Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/triviaqa","artifact":"https://unfragile.ai/triviaqa","verify":"https://unfragile.ai/api/v1/verify?slug=triviaqa","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"}}