SOTAVerified

Question Answering

Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.

( Image credit: SQuAD )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9276–9300 of 10817 papers

TitleStatusHype
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
INAOE\_UPV-CORE: Extracting Word Associations from Document Corpora to estimate Semantic Textual Similarity—0
InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models—0
In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT—0
In-Context Ability Transfer for Question Decomposition in Complex QA—0
In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models—0
In-Context Learning with Topological Information for Knowledge Graph Completion—0
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling—0
Incorporating External Knowledge into Machine Reading for Generative Question Answering—0
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks—0
Incorporating medical knowledge in BERT for clinical relation extraction—0
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection—0
Incorporating Selectional Preferences in Multi-hop Relation Extraction—0
Incorporating Trustiness and Collective Synonym/Contrastive Evidence into Taxonomy Construction—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!—0
Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention—0
Incremental Improvement of a Question Answering System by Re-ranking Answer Candidates using Machine Learning—0
Incremental Knowledge Based Question Answering—0
Incremental Reading for Question Answering—0
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction—0
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
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All datasetsSQuAD2.0SQuAD1.1HotpotQAPIQABoolQCOPATriviaQASQuAD1.1 devNatural QuestionsOpenBookQATruthfulQAMultiRC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94—Unverified
2FPNet (ensemble)EM90.87—Unverified
3IE-NetV2 (ensemble)EM90.86—Unverified
4SA-Net on Albert (ensemble)EM90.72—Unverified
5SA-Net-V2 (ensemble)EM90.68—Unverified
6FPNet (ensemble)EM90.6—Unverified
7Retro-Reader (ensemble)EM90.58—Unverified
8EntitySpanFocusV2 (ensemble)EM90.52—Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49—Unverified
10EntitySpanFocus+AT (ensemble)EM90.45—Unverified