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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4276–4300 of 10817 papers

TitleStatusHype
Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models—0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks—0
A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances—0
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models—0
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining—0
CLIP-Powered TASS: Target-Aware Single-Stream Network for Audio-Visual Question Answering—0
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
A Shared Task on Multimodal Machine Translation and Crosslingual Image Description—0
Formally Specifying the High-Level Behavior of LLM-Based Agents—0
Forewords—0
A Shallow Discourse Parsing System Based On Maximum Entropy Model—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
FoQA: A Faroese Question-Answering Dataset—0
CLINIQA: A Machine Intelligence Based Clinical Question Answering System—0
ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering—0
Fooling Vision and Language Models Despite Localization and Attention Mechanism—0
Font-Agent: Enhancing Font Understanding with Large Language Models—0
Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization—0
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?—0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings—0
Follow-Up Questions Improve Documents Generated by Large Language Models—0
Folheador: browsing through Portuguese semantic relations—0
Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona?—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