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 4426–4450 of 10817 papers

TitleStatusHype
Around the GLOBE: Numerical Aggregation Question-Answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks—0
Adapting Large Language Models to Domains via Reading Comprehension—0
A Biomedical Question Answering System in BioASQ 2017—0
FilterRAG: Zero-Shot Informed Retrieval-Augmented Generation to Mitigate Hallucinations in VQA—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
CIC: A Framework for Culturally-Aware Image Captioning—0
A Robustly Optimized BERT Pre-training Approach with Post-training—0
Fill-in-the-Blank: A Challenging Video Understanding Evaluation Framework—0
Filling a Knowledge Graph with a Crowd—0
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering—0
A Road-map Towards Explainable Question Answering A Solution for Information Pollution—0
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference—0
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms—0
FFA Sora, video generation as fundus fluorescein angiography simulator—0
Few-Shot VQA with Frozen LLMs: A Tale of Two Approaches—0
Few-shot Unified Question Answering: Tuning Models or Prompts?—0
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads—0
Choosing which to use? A study of distributional models for nominal lexical semantic classification—0
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation—0
Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems—0
Few-shot Query-Focused Summarization with Prefix-Merging—0
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering—0
Few-shot Policy (de)composition in Conversational Question Answering—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