World Knowledge: TriviaQA, NQ & SQuAD
Master world knowledge benchmarks and factual question answering evaluation
What are World Knowledge Benchmarks?
World knowledge benchmarks evaluate AI models' ability to access, retrieve, and reason with factual information about the world. These benchmarks test whether models can answer questions requiring specific knowledge about history, science, geography, culture, and current events beyond what can be inferred from context alone.
TriviaQA, Natural Questions (NQ), and SQuAD represent different aspects of factual QA: trivia-style questions, real user queries, and reading comprehension with evidence passages, providing comprehensive evaluation of knowledge capabilities.
TriviaQA: Trivia Questions with Evidence
Dataset Characteristics
Scale & Format
- • 650K question-answer pairs
- • 95K unique questions
- • Questions from 14 trivia websites
- • Evidence documents from Wikipedia & web
- • Average answer length: 2 words
Evaluation Modes
- • Wikipedia: Find answers in Wikipedia
- • Web: Use web search documents
- • Verified: Human-verified evidence
- • Unfiltered: All collected documents
Example TriviaQA Question
Natural Questions: Real User Queries
Real-World Question Answering
Natural Questions contains real queries issued to Google Search, making it more representative of actual information needs compared to artificially constructed questions.
Dataset Size
Answer Types
No Answer
Question Categories & Complexity
Factual Questions
- • "When was the Berlin Wall built?"
- • "Who is the current CEO of Tesla?"
- • "What is the capital of Australia?"
Complex Queries
- • "How many episodes are in Game of Thrones season 8?"
- • "What are the side effects of taking aspirin daily?"
- • "Best restaurants near Times Square NYC"
SQuAD: Reading Comprehension
Stanford Question Answering Dataset
SQuAD 1.1
- • 100K+ question-answer pairs
- • 500+ Wikipedia articles
- • All questions answerable
- • Exact answer span required
- • Human: 86.8% F1 score
SQuAD 2.0
- • 150K+ questions (50K new)
- • 50K unanswerable questions
- • Plausible but wrong answers
- • "No answer" detection required
- • Human: 89.5% F1 score
SQuAD Example
Comprehensive Evaluation Framework
Model Performance Comparison
| Model | TriviaQA | Natural Questions | SQuAD 1.1 | SQuAD 2.0 | Average |
|---|---|---|---|---|---|
| Human Performance | 80.0% | 87.0% | 86.8% | 89.5% | 85.8% |
| GPT-4 | 85.2% | 69.1% | 86.4% | 84.9% | 81.4% |
| Claude-3 Opus | 82.8% | 67.3% | 84.1% | 82.6% | 79.2% |
| PaLM 2 | 81.4% | 64.7% | 83.2% | 80.9% | 77.6% |
| Llama-2-70B | 79.1% | 62.4% | 80.7% | 78.3% | 75.1% |
Key Observations
- • TriviaQA: Models excel with explicit evidence
- • Natural Questions: Significant gap from human performance
- • SQuAD: Near-human performance achieved
- • Real queries harder than curated datasets
Challenges
- • Temporal knowledge (recent events)
- • Multi-hop reasoning
- • Ambiguous question interpretation
- • Knowledge base inconsistencies
Improvements
- • Retrieval-augmented generation
- • Knowledge graph integration
- • Fine-tuning on domain data
- • Multi-turn question clarification
Retrieval-Augmented Question Answering
RAG for World Knowledge
Retrieval-Augmented Generation (RAG) significantly improves performance on world knowledge benchmarks by combining parametric knowledge with external document retrieval, enabling access to up-to-date information.
RAG Benefits
- • Access to current information
- • Reduced hallucination
- • Transparent evidence sourcing
- • Domain-specific knowledge
Performance Gains
- • TriviaQA: +15-20% accuracy
- • Natural Questions: +10-15%
- • Better temporal knowledge
- • Improved factual consistency
Evaluation Best Practices
✅ DO
- • Use exact match and F1 score metrics
- • Evaluate on multiple answer formats
- • Test temporal knowledge currency
- • Include unanswerable questions
- • Report performance by question type
- • Use human evaluation for ambiguous cases
❌ DON'T
- • Only evaluate on exact string matches
- • Ignore knowledge cutoff dates
- • Mix retrieval and non-retrieval settings
- • Use outdated benchmark versions
- • Ignore question ambiguity effects
- • Skip error analysis by category