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 3676–3700 of 10817 papers

TitleStatusHype
Modular Blended Attention Network for Video Question Answering—0
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency—0
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life VideosCode0
Long Story Short: a Summarize-then-Search Method for Long Video Question AnsweringCode0
Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism—0
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs—0
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization—0
Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation—0
Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering—0
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities—0
XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision—0
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text—0
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis—0
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched PromptsCode1
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design—0
A Lightweight Method to Generate Unanswerable Questions in EnglishCode0
Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination—0
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing—0
KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering—0
Split-NER: Named Entity Recognition via Two Question-Answering-based ClassificationsCode1
Harvest Video Foundation Models via Efficient Post-Pretraining—0
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering—0
Generating Context-Aware Natural Answers for Questions in 3D ScenesCode0
EHRTutor: Enhancing Patient Understanding of Discharge Instructions—0
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal ImageryCode0
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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