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
FiVL: A Framework for Improved Vision-Language AlignmentCode0
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability—0
CodeRepoQA: A Large-scale Benchmark for Software Engineering Question AnsweringCode0
RACQUET: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMsCode0
A Concept-Centric Approach to Multi-Modality Learning—0
ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics—0
A Cognitive Ideation Support Framework using IBM Watson Services—0
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs—0
Consistency of Compositional Generalization across Multiple LevelsCode0
Question: How do Large Language Models perform on the Question Answering tasks? Answer:—0
When to Speak, When to Abstain: Contrastive Decoding with Abstention—0
RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment—0
On the Structural Memory of LLM AgentsCode0
Knowledge Graphs: The Future of Data Integration and Insightful Discovery—0
LLM-based Discriminative Reasoning for Knowledge Graph Question Answering—0
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and TasksCode0
Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal CluesCode0
Modality-Inconsistent Continual Learning of Multimodal Large Language Models—0
CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding—0
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&ACode0
LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-SteeringCode0
Advancements and Challenges in Bangla Question Answering Models: A Comprehensive Review—0
Precise Length Control in Large Language Models—0
CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology—0
ACE-M^3: Automatic Capability Evaluator for Multimodal Medical Models—0
Context Filtering with Reward Modeling in Question Answering—0
ConceptEdit: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning—0
Interpretable LLM-based Table Question Answering—0
Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track—0
AgentPS: Agentic Process Supervision for Multi-modal Content Quality Assurance through Multi-round QA—0
Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette—0
Patch-level Sounding Object Tracking for Audio-Visual Question Answering—0
VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation—0
MedG-KRP: Medical Graph Knowledge Representation ProbingCode0
Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning—0
NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries—0
Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
Benchmarking Table Comprehension In The Wild—0
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation—0
IQViC: In-context, Question Adaptive Vision Compressor for Long-term Video Understanding LMMs—0
Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QACode0
ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation—0
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering—0
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models—0
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse DomainsCode0
ViUniT: Visual Unit Tests for More Robust Visual Programming—0
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies—0
Progressive Multi-granular Alignments for Grounded Reasoning in Large Vision-Language ModelsCode0
Can We Generate Visual Programs Without Prompting LLMs?—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