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 4076–4100 of 10817 papers

TitleStatusHype
The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering SystemsCode0
Changing Answer Order Can Decrease MMLU Accuracy—0
TrustUQA: A Trustful Framework for Unified Structured Data Question AnsweringCode0
Length Optimization in Conformal PredictionCode0
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature DistillationCode0
Handling Ontology Gaps in Semantic ParsingCode0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
Context Matters: An Empirical Study of the Impact of Contextual Information in Temporal Question Answering Systems—0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit Reasoning and Precise Spatio-Temporal Retrieval—0
Sanskrit Knowledge-based Systems: Annotation and Computational Tools—0
Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and PromptsCode0
Entropy-Based Decoding for Retrieval-Augmented Large Language ModelsCode0
CaLMQA: Exploring culturally specific long-form question answering across 23 languagesCode0
Advancing Question Answering on Handwritten Documents: A State-of-the-Art Recognition-Based Model for HW-SQuAD—0
Zero-Shot Long-Form Video Understanding through Screenplay—0
Claude 3.5 Sonnet Model Card Addendum—0
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language UnderstandingCode0
Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness—0
GPT-4V Explorations: Mining Autonomous Driving—0
Directed Domain Fine-Tuning: Tailoring Separate Modalities for Specific Training Tasks—0
Modulating Language Model Experiences through Frictions—0
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs—0
Attention Instruction: Amplifying Attention in the Middle via PromptingCode0
Training-Free Exponential Context Extension via Cascading KV CacheCode0
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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