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 8351–8375 of 10817 papers

TitleStatusHype
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models—0
Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru—0
Generating Syntactic Paraphrases—0
Robust Question Answering Through Sub-part Alignment—0
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation—0
Robust Visual Question Answering: Datasets, Methods, and Future Challenges—0
Robust Visual Reasoning via Language Guided Neural Module Networks—0
ReWind: Understanding Long Videos with Instructed Learnable Memory—0
Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition—0
Role play-based question-answering by real users for building chatbots with consistent personalities—0
Romanian TimeBank: An Annotated Parallel Corpus for Temporal Information—0
Combining dependency information and generalization in a pattern-based approach to the classification of lexical-semantic relation instances—0
Generating clickbait spoilers with an ensemble of large language models—0
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images—0
Generating Answering Patterns from Factoid Arabic Questions—0
ALS at IJCNLP-2017 Task 5: Answer Localization System for Multi-Choice Question Answering in Exams—0
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization—0
Revisiting the Superficial Alignment Hypothesis—0
Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer—0
A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World—0
Generating Answer Candidates for Quizzes and Answer-Aware Question Generators—0
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation—0
RS-MoE: Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering—0
RS-RAG: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model—0
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.—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