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 1001–1050 of 10817 papers

TitleStatusHype
Language Models with Image Descriptors are Strong Few-Shot Video-Language LearnersCode1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQACode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
LaPA: Latent Prompt Assist Model For Medical Visual Question AnsweringCode1
A Simple LLM Framework for Long-Range Video Question-AnsweringCode1
Large Language Models are Pretty Good Zero-Shot Video Game Bug DetectorsCode1
Dynamically Fused Graph Network for Multi-hop ReasoningCode1
Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language BootstrappingCode1
Large Language Models for Scientific Synthesis, Inference and ExplanationCode1
Benchmarking large language models for biomedical natural language processing applications and recommendationsCode1
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument ExtractionCode1
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question AnsweringCode1
AllenAct: A Framework for Embodied AI ResearchCode1
Asking Clarification Questions to Handle Ambiguity in Open-Domain QACode1
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question AnsweringCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairsCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Latent Compositional Representations Improve Systematic Generalization in Grounded Question AnsweringCode1
LaTr: Layout-Aware Transformer for Scene-Text VQACode1
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question AnsweringCode1
LAVENDER: Unifying Video-Language Understanding as Masked Language ModelingCode1
A Survey on Efficient Vision-Language ModelsCode1
A Few More Examples May Be Worth Billions of ParametersCode1
Dual-Key Multimodal Backdoors for Visual Question AnsweringCode1
DyGKT: Dynamic Graph Learning for Knowledge TracingCode1
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument ExtractionCode1
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge BasesCode1
AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label EmbeddingCode1
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related QueriesCode1
An Investigation of LLMs' Inefficacy in Understanding Converse RelationsCode1
DSPNet: Dual-vision Scene Perception for Robust 3D Question AnsweringCode1
Are Bias Mitigation Techniques for Deep Learning Effective?Code1
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical DomainCode1
DREAM: Improving Situational QA by First Elaborating the SituationCode1
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesCode1
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World EnvironmentsCode1
DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace EditingCode1
DocVXQA: Context-Aware Visual Explanations for Document Question AnsweringCode1
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific InformationCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented GenerationCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and IntentsCode1
Distinguishing Ignorance from Error in LLM HallucinationsCode1
Ditch the Gold Standard: Re-evaluating Conversational Question AnsweringCode1
DocNLI: A Large-scale Dataset for Document-level Natural Language InferenceCode1
DOM-LM: Learning Generalizable Representations for HTML DocumentsCode1
Efficiently Tuned Parameters are Task EmbeddingsCode1
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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