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 6001–6025 of 10817 papers

TitleStatusHype
M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
Hyperbolic Attention Networks—0
Dual Embeddings and Metrics for Relational Similarity—0
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications—0
Machine Comprehension with Discourse Relations—0
Machine Comprehension with Syntax, Frames, and Semantics—0
Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases—0
Controlled Natural Languages and Default Reasoning—0
Machine Reading Comprehension: Generative or Extractive Reader?—0
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings—0
Machine Translation Evaluation Meets Community Question Answering—0
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset—0
Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers—0
Hybrid Question Answering over Knowledge Base and Free Text—0
Hybrid Graphs for Table-and-Text based Question Answering using LLMs—0
DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning—0
MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning—0
Contri(e)ve: Context + Retrieve for Scholarly Question Answering—0
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior—0
A Syntactic Approach to Domain-Specific Automatic Question Generation—0
An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.—0
Contributions to the Improvement of Question Answering Systems in the Biomedical Domain—0
Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets—0
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases—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