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 7901–7925 of 10817 papers

TitleStatusHype
Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation—0
Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering—0
Inverse Visual Question Answering with Multi-Level Attentions—0
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering—0
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool—0
Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation—0
Automated Answer Validation using Text Similarity—0
QUINT: Interpretable Question Answering over Knowledge Bases—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs—0
Introduction of a Probabilistic Language Model to Non-Factoid Question Answering Using Example Q\&A Pairs—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types—0
QurAna: Corpus of the Quran annotated with Pronominal Anaphora—0
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering—0
Introduction method for argumentative dialogue using paired question-answering interchange about personality—0
Introducing Semantics into Speech Encoders—0
Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction—0
CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task—0
R3: A Reading Comprehension Benchmark Requiring Reasoning Processes—0
R3 : Refined Retriever-Reader pipeline for Multidoc2dial—0
Introducing "Forecast Utterance" for Conversational Data Science—0
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models—0
RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training—0
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass—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