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 3051–3100 of 10817 papers

TitleStatusHype
Hypo3D: Exploring Hypothetical Reasoning in 3D—0
VLM-Assisted Continual learning for Visual Question Answering in Self-Driving—0
Multilingual State Space Models for Structured Question Answering in Indic LanguagesCode0
Memory-Efficient Fine-Tuning of Transformers via Token SelectionCode0
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering—0
General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance—0
Hybrid Graphs for Table-and-Text based Question Answering using LLMs—0
InnerThoughts: Disentangling Representations and Predictions in Large Language Models—0
Anatomy Might Be All You Need: Forecasting What to Do During Surgery—0
Cross-Language Approach for Quranic QA—0
Multiple Abstraction Level Retrieve Augment Generation—0
3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow—0
Comprehensive Evaluation for a Large Scale Knowledge Graph Question Answering Service—0
PISCO: Pretty Simple Compression for Retrieval-Augmented Generation—0
Large Models in Dialogue for Active Perception and Anomaly DetectionCode0
URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT—0
A Comprehensive Study on Fine-Tuning Large Language Models for Medical Question Answering Using Classification Models and Comparative Analysis—0
Provence: efficient and robust context pruning for retrieval-augmented generation—0
Options-Aware Dense Retrieval for Multiple-Choice query Answering—0
Adapting Biomedical Abstracts into Plain language using Large Language Models—0
How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback—0
Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis—0
Complete Chess Games Enable LLM Become A Chess Master—0
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs—0
LLM Evaluation Based on Aerospace Manufacturing Expertise: Automated Generation and Multi-Model Question Answering—0
An Attempt to Unraveling Token Prediction Refinement and Identifying Essential Layers of Large Language Models—0
Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training—0
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval—0
Towards Better Understanding Table Instruction Tuning: Decoupling the Effects from Data versus Models—0
Chain-of-Retrieval Augmented Generation—0
Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMsCode0
MASTER: A Multi-Agent System with LLM Specialized MCTS—0
Interpretability Analysis of Domain Adapted Dense Retrievers—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
ENTER: Event Based Interpretable Reasoning for VideoQA—0
Scene Understanding Enabled Semantic Communication with Open Channel Coding—0
Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metrics—0
K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected CompressorCode0
RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question AnsweringCode0
Question Answering on Patient Medical Records with Private Fine-Tuned LLMs—0
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process—0
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization—0
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
Patent Figure Classification using Large Vision-language ModelsCode0
Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering—0
Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home—0
An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts—0
Few-shot Policy (de)composition in Conversational Question Answering—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