SOTAVerified

Open-Domain Question Answering

Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.

Papers

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Showing 1–10 of 494 papers

TitleStatusHype
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document ReasoningCode2
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-k—0
ECoRAG: Evidentiality-guided Compression for Long Context RAGCode1
GenKI: Enhancing Open-Domain Question Answering with Knowledge Integration and Controllable Generation in Large Language ModelsCode0
NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement LearningCode1
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization—0
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation—0
Scaling Reasoning can Improve Factuality in Large Language ModelsCode0
Benchmarking LLM-based Relevance Judgment MethodsCode0
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task—0
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All datasetsKILT: ELI5KILT: Natural QuestionsKILT: TriviaQAKILT: HotpotQASearchQAELI5QUASARNatural QuestionsSQuAD1.1 devWebQuestionsSQuAD1.1DuReader

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Evidence Aggregation via R^3 Re-RankingEM (Quasar-T)42.3—Unverified
2Denoising QAEM (Quasar-T)42.2—Unverified
3DecaPropEM (Quasar-T)38.6—Unverified
4R^3EM (Quasar-T)35.3—Unverified
5GAEM (Quasar-T)26.4—Unverified
6BiDAFEM (Quasar-T)25.9—Unverified