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 5551–5600 of 10817 papers

TitleStatusHype
DUBLIN -- Document Understanding By Language-Image Network—0
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database—0
Pre-training Language Models for Comparative Reasoning—0
BAND: Biomedical Alert News DatasetCode0
Language Models with Rationality—0
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language ModelsCode0
Knowledge Graphs Querying—0
Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach—0
IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions—0
i-Code Studio: A Configurable and Composable Framework for Integrative AI—0
Continual Dialogue State Tracking via Example-Guided Question AnsweringCode0
HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision—0
Make a Choice! Knowledge Base Question Answering with In-Context Learning—0
REFinD: Relation Extraction Financial DatasetCode0
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending—0
FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering—0
Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge GraphCode0
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond—0
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure AbductionCode0
Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-SupervisionCode0
Teaching Probabilistic Logical Reasoning to TransformersCode0
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies—0
Model Analysis & Evaluation for Ambiguous Question AnsweringCode0
Pruning Pre-trained Language Models with Principled Importance and Self-regularizationCode0
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual ScenariosCode0
Continually Improving Extractive QA via Human FeedbackCode0
Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes—0
Exploring the Viability of Synthetic Query Generation for Relevance Prediction—0
Evaluation of medium-large Language Models at zero-shot closed book generative question answering—0
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing—0
Graphologue: Exploring Large Language Model Responses with Interactive Diagrams—0
Writing your own book: A method for going from closed to open book QA to improve robustness and performance of smaller LLMs—0
Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature—0
TG-VQA: Ternary Game of Video Question Answering—0
PaLM 2 Technical Report—0
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries—0
An Empirical Study on the Language Modal in Visual Question Answering—0
IMAD: IMage-Augmented multi-modal DialogueCode0
Probing the Role of Positional Information in Vision-Language Models—0
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero ShotCode0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
Is a Video worth n n Images? A Highly Efficient Approach to Transformer-based Video Question Answering—0
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech TranslationCode0
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text—0
MeeQA: Natural Questions in Meeting TranscriptsCode0
Capturing Humans' Mental Models of AI: An Item Response Theory ApproachCode0
Question-Answering System Extracts Information on Injection Drug Use from Clinical NotesCode0
Semantic Composition in Visually Grounded Language Models—0
KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question AnsweringCode0
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance—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