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 4176–4200 of 10817 papers

TitleStatusHype
From Textual Entailment to Knowledgeable Machines—0
From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics—0
Coarse-to-Fine Question Answering for Long Documents—0
From text to multimodal: a survey of adversarial example generation in question answering systems—0
From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems—0
Coarse-to-Fine Query Focused Multi-Document Summarization—0
From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason—0
From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering—0
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models—0
From Shakespeare to Twitter: What are Language Styles all about?—0
Coarse to Fine Grained Sense Disambiguation in Wikipedia—0
Asking More Informative Questions for Grounded Retrieval—0
Adaptive Memory Networks—0
AcademicGPT: Empowering Academic Research—0
From Retrieval to Generation: Comparing Different Approaches—0
Coarse-to-Fine Attention Models for Document Summarization—0
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents—0
Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering—0
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries—0
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources—0
Coarse Lexical Frame Acquisition at the Syntax--Semantics Interface Using a Latent-Variable PCFG Model—0
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing—0
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering—0
Asking Clarification Questions in Knowledge-Based Question Answering—0
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—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