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 3251–3275 of 10817 papers

TitleStatusHype
BAT: Learning to Reason about Spatial Sounds with Large Language Models—0
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA—0
SPARQL Generation with Entity Pre-trained GPT for KG Question AnsweringCode0
MEIA: Multimodal Embodied Perception and Interaction in Unknown EnvironmentsCode5
Instruction Makes a DifferenceCode0
An Exam-based Evaluation Approach Beyond Traditional Relevance Judgments—0
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains—0
Instruction-Guided Scene Text Recognition—0
Can Generative AI Support Patients' & Caregivers' Informational Needs? Towards Task-Centric Evaluation Of AI Systems—0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
PipeNet: Question Answering with Semantic Pruning over Knowledge GraphsCode0
RAPTOR: Recursive Abstractive Processing for Tree-Organized RetrievalCode5
Desiderata for the Context Use of Question Answering Systems—0
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity AnalysisCode0
QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming LearnersCode0
Security and Privacy Challenges of Large Language Models: A Survey—0
YTCommentQA: Video Question Answerability in Instructional VideosCode0
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks—0
Taxonomy of Mathematical PlagiarismCode0
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language ModelsCode3
Towards Optimizing the Costs of LLM Usage—0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA—0
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table ExpansionCode1
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—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