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 3301–3325 of 10817 papers

TitleStatusHype
In-Context Learning with Topological Information for Knowledge Graph Completion—0
Discrete Subgraph Sampling for Interpretable Graph based Visual Question AnsweringCode0
DialogAgent: An Auto-engagement Agent for Code Question Answering Data Production—0
Barking Up The Syntactic Tree: Enhancing VLM Training with Syntactic Losses—0
A Multimodal Social Agent—0
Progressive Multi-granular Alignments for Grounded Reasoning in Large Vision-Language ModelsCode0
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
RAG-based Question Answering over Heterogeneous Data and Text—0
MM-PoE: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal ModelsCode0
Piece of Table: A Divide-and-Conquer Approach for Selecting Sub-Tables in Table Question Answering—0
AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework—0
Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education—0
3D Spatial Understanding in MLLMs: Disambiguation and Evaluation—0
FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question AnsweringCode0
Ranked from Within: Ranking Large Multimodal Models for Visual Question Answering Without Labels—0
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language ModelsCode0
1-800-SHARED-TASKS at RegNLP: Lexical Reranking of Semantic Retrieval (LeSeR) for Regulatory Question Answering—0
An Entailment Tree Generation Approach for Multimodal Multi-Hop Question Answering with Mixture-of-Experts and Iterative Feedback Mechanism—0
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design—0
SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering—0
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks—0
Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph based Question-Answering Agent—0
SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering—0
Steps are all you need: Rethinking STEM Education with Prompt Engineering—0
Knowledge Graphs are all you need: Leveraging KGs in Physics 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