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 9251–9300 of 10817 papers

TitleStatusHype
Improving Question Answering over Knowledge Graphs Using Graph Summarization—0
PolQA: Polish Question Answering Dataset—0
Improving Question Answering with Generation of NQ-like Questions—0
Improving Question Classification by Feature Extraction and Selection—0
Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs—0
Improving Retrieval for RAG based Question Answering Models on Financial Documents—0
Improving Semantic Parsing via Answer Type Inference—0
Improving Skin Condition Classification with a Question Answering Model—0
Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning—0
Improving Span-based Question Answering Systems with Coarsely Labeled Data—0
Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs—0
Improving Temporal Relation Extraction with Training Instance Augmentation—0
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering—0
Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features—0
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs—0
Improving Twitter Named Entity Recognition using Word Representations—0
Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings—0
Improving Unsupervised Question Answering via Summarization-Informed Question Generation—0
Improving Users' Mental Model with Attention-directed Counterfactual Edits—0
Improving Vietnamese Legal Document Retrieval using Synthetic Data—0
Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment—0
Improving Visual Question Answering by Referring to Generated Paragraph Captions—0
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions—0
Improving VQA and its Explanations \\ by Comparing Competing Explanations—0
Improving Word Representations via Global Context and Multiple Word Prototypes—0
Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph—0
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
INAOE\_UPV-CORE: Extracting Word Associations from Document Corpora to estimate Semantic Textual Similarity—0
InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models—0
In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT—0
In-Context Ability Transfer for Question Decomposition in Complex QA—0
In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models—0
In-Context Learning with Topological Information for Knowledge Graph Completion—0
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling—0
Incorporating External Knowledge into Machine Reading for Generative Question Answering—0
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks—0
Incorporating medical knowledge in BERT for clinical relation extraction—0
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection—0
Incorporating Selectional Preferences in Multi-hop Relation Extraction—0
Incorporating Trustiness and Collective Synonym/Contrastive Evidence into Taxonomy Construction—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!—0
Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention—0
Incremental Improvement of a Question Answering System by Re-ranking Answer Candidates using Machine Learning—0
Incremental Knowledge Based Question Answering—0
Incremental Reading for Question Answering—0
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction—0
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages—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