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 3251–3300 of 10817 papers

TitleStatusHype
BAT: Learning to Reason about Spatial Sounds with Large Language Models—0
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA—0
SPARQL Generation with Entity Pre-trained GPT for KG Question AnsweringCode0
Instruction Makes a DifferenceCode0
MEIA: Multimodal Embodied Perception and Interaction in Unknown EnvironmentsCode5
An Exam-based Evaluation Approach Beyond Traditional Relevance Judgments—0
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains—0
Can Generative AI Support Patients' & Caregivers' Informational Needs? Towards Task-Centric Evaluation Of AI Systems—0
RAPTOR: Recursive Abstractive Processing for Tree-Organized RetrievalCode5
PipeNet: Question Answering with Semantic Pruning over Knowledge GraphsCode0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
Instruction-Guided Scene Text Recognition—0
Desiderata for the Context Use of Question Answering Systems—0
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity AnalysisCode0
QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming LearnersCode0
Security and Privacy Challenges of Large Language Models: A Survey—0
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks—0
YTCommentQA: Video Question Answerability in Instructional VideosCode0
Taxonomy of Mathematical PlagiarismCode0
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language ModelsCode3
Towards Optimizing the Costs of LLM Usage—0
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA—0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering—0
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table ExpansionCode1
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—0
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models—0
A RAG-based Question Answering System Proposal for Understanding Islam: MufassirQAS LLM—0
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language ModelsCode2
DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure—0
Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance—0
Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs—0
LongHealth: A Question Answering Benchmark with Long Clinical DocumentsCode1
Genie: Achieving Human Parity in Content-Grounded Datasets Generation—0
Towards Consistent Natural-Language Explanations via Explanation-Consistency FinetuningCode0
Question answering systems for health professionals at the point of care -- a systematic review—0
IICONGRAPH: improved Iconographic and Iconological Statements in Knowledge Graphs—0
CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering—0
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement LearningCode1
It's About Time: Incorporating Temporality in Retrieval Augmented Language Models—0
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering—0
InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with InstructionsCode2
Fine-Grained Stateful Knowledge Exploration: A Novel Paradigm for Integrating Knowledge Graphs with Large Language ModelsCode0
Can AI Assistants Know What They Don't Know?Code2
Graph Guided Question Answer Generation for Procedural Question-Answering—0
Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes—0
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data—0
TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic TasksCode1
Free Form Medical Visual Question Answering in Radiology—0
LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-ExplanationsCode1
Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context—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