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 3026–3050 of 10817 papers

TitleStatusHype
Performance Analysis of Traditional VQA Models Under Limited Computational Resources—0
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical ImagesCode0
Self-Training Large Language Models for Tool-Use Without Demonstrations—0
Investigating the Shortcomings of LLMs in Step-by-Step Legal ReasoningCode0
Evolving LLMs' Self-Refinement Capability via Iterative Preference Optimization—0
Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency—0
ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and ReasoningCode0
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment—0
Efficient Knowledge Feeding to Language Models: A Novel Integrated Encoder-Decoder Architecture—0
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents—0
No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only MemoryCode0
DocMIA: Document-Level Membership Inference Attacks against DocVQA ModelsCode0
TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives—0
Efficient Few-Shot Continual Learning in Vision-Language Models—0
SMI: An Information-Theoretic Metric for Predicting Model Knowledge Solely from Pre-Training SignalsCode0
Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question AnsweringCode0
LLMs to Support a Domain Specific Knowledge Assistant—0
SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions—0
LLMs can be easily Confused by Instructional Distractions—0
Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions—0
Exploring Spatial Language Grounding Through Referring Expressions—0
AmaSQuAD: A Benchmark for Amharic Extractive Question Answering—0
ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual Attribution—0
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization—0
Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models—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