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 10326–10350 of 10817 papers

TitleStatusHype
TABi: Type-Aware Bi-encoders for End-to-End Entity Retrieval—0
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering—0
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT—0
TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering—0
TableQA: Question Answering on Tabular Data—0
Table-R1: Region-based Reinforcement Learning for Table Understanding—0
Table Retrieval Does Not Necessitate Table-specific Model Design—0
Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs—0
Tables as Semi-structured Knowledge for Question Answering—0
TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions—0
TabSD: Large Free-Form Table Question Answering with SQL-Based Table Decomposition—0
Tabular-TX: Theme-Explanation Structure-based Table Summarization via In-Context Learning—0
Tackling Adversarial Examples in QA via Answer Sentence Selection—0
Tackling Biomedical Text Summarization: OAQA at BioASQ 5B—0
Tackling Code-Switched NER: Participation of CMU—0
Tackling VQA with Pretrained Foundation Models without Further Training—0
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning—0
TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering—0
Take A Step Back: Rethinking the Two Stages in Visual Reasoning—0
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA—0
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded—0
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning—0
TALE: A Tool-Augmented Framework for Reference-Free Evaluation of Large Language Models—0
Talking to GDELT Through Knowledge Graphs—0
Talking to the brain: Using Large Language Models as Proxies to Model Brain Semantic Representation—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