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 1051–1100 of 10817 papers

TitleStatusHype
Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering—0
Few-shot Policy (de)composition in Conversational Question Answering—0
InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language ModelsCode1
A Collection of Question Answering Datasets for Norwegian—0
Leveraging Chain of Thought towards Empathetic Spoken Dialogue without Corresponding Question-Answering Data—0
A Method for Multi-Hop Question Answering on Persian Knowledge Graph—0
Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!—0
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring—0
Tabular-TX: Theme-Explanation Structure-based Table Summarization via In-Context Learning—0
Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMsCode0
Passage Segmentation of Documents for Extractive Question Answering—0
Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems—0
A Simple Aerial Detection Baseline of Multimodal Language ModelsCode2
Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such as Kiswahili—0
Perspective Transition of Large Language Models for Solving Subjective Tasks—0
Augmenting a Large Language Model with a Combination of Text and Visual Data for Conversational Visualization of Global Geospatial Data—0
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness—0
To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation—0
Embodied Scene Understanding for Vision Language Models via MetaVQA—0
Dynamic Knowledge Integration for Enhanced Vision-Language Reasoning—0
Towards Multilingual LLM Evaluation for Baltic and Nordic languages: A study on Lithuanian History—0
SteLLA: A Structured Grading System Using LLMs with RAG—0
Admitting Ignorance Helps the Video Question Answering Models to Answer—0
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning—0
Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering—0
Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video UnderstandingCode4
SAR Strikes Back: A New Hope for RSVQA—0
Advice for Diabetes Self-Management by ChatGPT Models: Challenges and Recommendations—0
ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems—0
Investigating Energy Efficiency and Performance Trade-offs in LLM Inference Across Tasks and DVFS Settings—0
WebWalker: Benchmarking LLMs in Web TraversalCode11
The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering—0
MECD+: Unlocking Event-Level Causal Graph Discovery for Video ReasoningCode1
Parallel Key-Value Cache Fusion for Position Invariant RAG—0
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel TrainingCode0
TimeLogic: A Temporal Logic Benchmark for Video QA—0
Language Fusion for Parameter-Efficient Cross-lingual TransferCode0
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing—0
Fine-tuning Large Language Models for Improving Factuality in Legal Question AnsweringCode0
First Token Probability Guided RAG for Telecom Question Answering—0
Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations—0
PEACE: Empowering Geologic Map Holistic Understanding with MLLMs—0
Multi-Agent Collaboration Mechanisms: A Survey of LLMsCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks—0
Overcoming Language Priors for Visual Question Answering Based on Knowledge Distillation—0
How to Tune a Multilingual Encoder Model for Germanic Languages: A Study of PEFT, Full Fine-Tuning, and Language AdaptersCode0
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition BenchmarkCode1
Search-o1: Agentic Search-Enhanced Large Reasoning ModelsCode5
LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding—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