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 2951–2975 of 10817 papers

TitleStatusHype
MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models—0
Argument-Based Comparative Question Answering Evaluation Benchmark—0
MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models—0
Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation FrameworkCode0
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?Code0
On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation SystemsCode0
NLP-AKG: Few-Shot Construction of NLP Academic Knowledge Graph Based on LLM—0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison—0
Effects of Prompt Length on Domain-specific Tasks for Large Language Models—0
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering—0
Is Relevance Propagated from Retriever to Generator in RAG?—0
Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests—0
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts—0
MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention HeadsCode0
REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models—0
MCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering—0
Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation—0
PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language ModelsCode0
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue—0
Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning—0
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning—0
Quantifying Memorization and Retriever Performance in Retrieval-Augmented Vision-Language Models—0
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering—0
PitVQA++: Vector Matrix-Low-Rank Adaptation for Open-Ended Visual Question Answering in Pituitary SurgeryCode0
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above—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