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 4676–4700 of 10817 papers

TitleStatusHype
Location-Free Scene Graph GenerationCode1
COVID-19 event extraction from Twitter via extractive question answering with continuous promptsCode1
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question AnsweringCode0
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningCode1
A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering—0
Can AI-Generated Text be Reliably Detected?Code1
Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data—0
Secret-Keeping in Question Answering—0
Logical Implications for Visual Question Answering ConsistencyCode0
GPT-4 Technical ReportCode6
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM FamilyCode1
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region—0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
Vision-Language Models as Success Detectors—0
ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual DescriptionsCode2
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension—0
MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling—0
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language ModelsCode1
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Toward Unsupervised Realistic Visual Question Answering—0
Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?—0
Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing—0
Does Synthetic Data Generation of LLMs Help Clinical Text Mining?—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