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 6151–6200 of 10817 papers

TitleStatusHype
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction—0
MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering—0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation—0
A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions—0
MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering—0
MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering—0
How Well can We Learn Interpretable Entity Types from Text?—0
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark—0
MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering—0
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
MedGo: A Chinese Medical Large Language Model—0
MedGPT: Medical Concept Prediction from Clinical Narratives—0
MedHal: An Evaluation Dataset for Medical Hallucination Detection—0
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements—0
MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models—0
MeDiaQA: A Question Answering Dataset on Medical Dialogues—0
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures—0
Medical Exam Question Answering with Large-scale Reading Comprehension—0
Medical Knowledge-enriched Textual Entailment Framework—0
ECNU: Using Multiple Sources of CQA-based Information for Answers Selection and YES/NO Response Inference—0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
Medical Relation Extraction with Manifold Models—0
Medical Visual Question Answering: A Survey—0
Medical visual question answering using joint self-supervised learning—0
How Transferable are Reasoning Patterns in VQA?—0
Medicines Question Answering System, MeQA—0
Continuous Perception Benchmark—0
MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications—0
A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead—0
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing—0
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input—0
m-KAILIN: Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training—0
How to Seq2seq for SQL—0
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering—0
Continuous Active Learning Using Pretrained Transformers—0
How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback—0
How to make qubits speak—0
A Survey on Semantic Parsing from the perspective of Compositionality—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility—0
MedPAIR: Measuring Physicians and AI Relevance Alignment in Medical Question Answering—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
Answer Generation for Retrieval-based Question Answering Systems—0
MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering—0
How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos—0
Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities—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