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

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Showing 3901–3925 of 10817 papers

TitleStatusHype
Making Retrieval-Augmented Language Models Robust to Irrelevant ContextCode1
RA-DIT: Retrieval-Augmented Dual Instruction Tuning—0
Representation Engineering: A Top-Down Approach to AI TransparencyCode2
Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence—0
A Framework for Inference Inspired by Human Memory MechanismsCode1
Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks—0
Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging—0
A Novel Computational and Modeling Foundation for Automatic Coherence Assessment—0
SLM: Bridge the thin gap between speech and text foundation models—0
Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life using Mental Health Forums Data—0
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question AnsweringCode2
Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models—0
Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge DistillationCode0
Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language ModelsCode1
Using Weak Supervision and Data Augmentation in Question Answering—0
Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering SystemsCode1
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration—0
At Which Training Stage Does Code Data Help LLMs Reasoning?Code1
VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language ModelsCode1
Toloka Visual Question Answering BenchmarkCode1
Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving—0
Question answering using deep learning in low resource Indian language Marathi—0
MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering—0
Tackling VQA with Pretrained Foundation Models without Further Training—0
Zero-Shot and Few-Shot Video Question Answering with Multi-Modal PromptsCode1
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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