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 3526–3550 of 10817 papers

TitleStatusHype
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback—0
Universal Self-Consistency for Large Language Model Generation—0
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement LearningCode0
Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models—0
Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering—0
AviationGPT: A Large Language Model for the Aviation Domain—0
How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)—0
E-ViLM: Efficient Video-Language Model via Masked Video Modeling with Semantic Vector-Quantized Tokenizer—0
Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language ModelsCode1
Ascle: A Python Natural Language Processing Toolkit for Medical Text GenerationCode1
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in MedicineCode5
LLaMA-VID: An Image is Worth 2 Tokens in Large Language ModelsCode2
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation—0
Characterizing Video Question Answering with Sparsified Inputs—0
Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges—0
Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models—0
A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling—0
LLMGA: Multimodal Large Language Model based Generation AssistantCode2
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language ModelsCode1
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language ModelsCode1
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge GraphsCode1
Optimizing and Fine-tuning Large Language Model for Urban Renewal—0
Fully Authentic Visual Question Answering Dataset from Online CommunitiesCode0
MEDITRON-70B: Scaling Medical Pretraining for Large Language ModelsCode4
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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