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 9551–9600 of 10817 papers

TitleStatusHype
Language Models Can See Better: Visual Contrastive Decoding For LLM Multimodal ReasoningCode0
FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story VideosCode0
Consistency of Compositional Generalization across Multiple LevelsCode0
Language Models Still Struggle to Zero-shot Reason about Time SeriesCode0
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domainCode0
ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive PretrainingCode0
Russian Jeopardy! Data Set for Question-Answering SystemsCode0
FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with FreebaseCode0
FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question AnsweringCode0
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question AnsweringCode0
Frame- and Entity-Based Knowledge for Common-Sense Argumentative ReasoningCode0
Conditioning LSTM Decoder and Bi-directional Attention Based Question Answering SystemCode0
Abductive Commonsense ReasoningCode0
Robust and Scalable Differentiable Neural Computer for Question AnsweringCode0
Phrase-Indexed Question Answering: A New Challenge for Scalable Document ComprehensionCode0
Multimodal Residual Learning for Visual QACode0
Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive LearningCode0
Large Language Models and Multimodal Retrieval for Visual Word Sense DisambiguationCode0
ForPKG: A Framework for Constructing Forestry Policy Knowledge Graph and Application AnalysisCode0
BERT Based Multilingual Machine Comprehension in English and HindiCode0
Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on WikipediaCode0
FormulaReasoning: A Dataset for Formula-Based Numerical ReasoningCode0
Foreseeing the Benefits of Incidental SupervisionCode0
Concise Answers to Complex Questions: Summarization of Long-form AnswersCode0
Large Language Models as Interpolated and Extrapolated Event PredictorsCode0
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge GraphsCode0
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question AnsweringCode0
BEnQA: A Question Answering and Reasoning Benchmark for Bengali and EnglishCode0
Focus, Merge, Rank: Improved Question Answering Based on Semi-structured Knowledge BasesCode0
Focal Visual-Text Attention for Visual Question AnsweringCode0
Question Answering over Linked Data with GPT-3Code0
Composition Vision-Language Understanding via Segment and Depth Anything ModelCode0
Focal Visual-Text Attention for Memex Question AnsweringCode0
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure AbductionCode0
FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question AnsweringCode0
FlowQA: Grasping Flow in History for Conversational Machine ComprehensionCode0
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic SearchCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Large Language Models Need Holistically Thought in Medical Conversational QACode0
FiVL: A Framework for Improved Vision-Language AlignmentCode0
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP ConceptsCode0
Fine Tuning vs. Retrieval Augmented Generation for Less Popular KnowledgeCode0
Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question AnsweringCode0
Large Language Models Understand LayoutCode0
Fine-tuning Large Language Models for Improving Factuality in Legal Question AnsweringCode0
Large Models in Dialogue for Active Perception and Anomaly DetectionCode0
ReDiT: Re‑evaluating large visual question answering model confidence by defining input scenario Difficulty and applying Temperature mappingCode0
A review of Spanish corpora annotated with negationCode0
MultiOCR-QA: Dataset for Evaluating Robustness of LLMs in Question Answering on Multilingual OCR TextsCode0
Fine-Tuning and Retrieval Augmented Generation for Question Answering Using Affordable Large Language ModelsCode0
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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