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 1901–1925 of 10817 papers

TitleStatusHype
An Evaluation Framework for Attributed Information Retrieval using Large Language ModelsCode0
Top-down Activity Representation Learning for Video Question Answering—0
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG—0
Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources—0
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering—0
Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources—0
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric KnowledgeCode1
MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications—0
Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial AttacksCode0
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering—0
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented GenerationCode9
VisScience: An Extensive Benchmark for Evaluating K12 Educational Multi-modal Scientific Reasoning—0
GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question AnsweringCode1
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models—0
LIME: Less Is More for MLLM EvaluationCode1
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review—0
Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding—0
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysisCode2
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning—0
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA frameworkCode1
Seek and Solve Reasoning for Table Question Answering—0
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge DiscoveryCode7
Breaking Neural Network Scaling Laws with Modularity—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
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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