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 1376–1400 of 10817 papers

TitleStatusHype
Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark—0
COLD: Causal reasOning in cLosed Daily activitiesCode0
TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with Scalable Context and Symbolic ExtensionCode0
Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs—0
Sparse Attention Vectors: Generative Multimodal Model Features Are Discriminative Vision-Language Classifiers—0
Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs—0
DIESEL -- Dynamic Inference-Guidance via Evasion of Semantic Embeddings in LLMs—0
ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?—0
Active Data Curation Effectively Distills Large-Scale Multimodal Models—0
3D Scene Graph Guided Vision-Language Pre-training—0
GeneQuery: A General QA-based Framework for Spatial Gene Expression Predictions from Histology ImagesCode0
Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation—0
DRS: Deep Question Reformulation With Structured OutputCode0
Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation—0
VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction FormatCode1
Cross-modal Information Flow in Multimodal Large Language ModelsCode1
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation—0
Efficient Multi-modal Large Language Models via Visual Token Grouping—0
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question AnsweringCode2
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey—0
Scaling Speech-Text Pre-training with Synthetic Interleaved DataCode7
Grounding-IQA: Multimodal Language Grounding Model for Image Quality AssessmentCode2
Task Progressive Curriculum Learning for Robust Visual 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