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 3001–3025 of 10817 papers

TitleStatusHype
Beyond Text: Frozen Large Language Models in Visual Signal ComprehensionCode2
Beyond Memorization: The Challenge of Random Memory Access in Language ModelsCode1
Complex Reasoning over Logical Queries on Commonsense Knowledge GraphsCode1
InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language ModelsCode1
SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Casual InferenceCode0
ERA-CoT: Improving Chain-of-Thought through Entity Relationship AnalysisCode2
ALaRM: Align Language Models via Hierarchical Rewards ModelingCode1
Answering Diverse Questions via Text Attached with Key Audio-Visual CluesCode0
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification—0
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking TechniquesCode2
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs—0
Calibrating Large Language Models Using Their Generations OnlyCode1
Debiasing Multimodal Large Language ModelsCode2
ChatASU: Evoking LLM's Reflexion to Truly Understand Aspect Sentiment in Dialogues—0
Can't Remember Details in Long Documents? You Need Some R&RCode1
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextCode3
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-ThoughtCode1
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question AnsweringCode1
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answeringCode0
TextMonkey: An OCR-Free Large Multimodal Model for Understanding DocumentCode5
QAQ: Quality Adaptive Quantization for LLM KV CacheCode2
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual ScenariosCode2
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM—0
Advancing Chinese biomedical text mining with community challenges—0
Effectiveness Assessment of Recent Large Vision-Language Models—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