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 2851–2900 of 10817 papers

TitleStatusHype
BRIT: Bidirectional Retrieval over Unified Image-Text Graph—0
Answer ranking in Community Question Answering: a deep learning approach—0
Bring Remote Sensing Object Detect Into Nature Language Model: Using SFT Method—0
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling—0
A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering—0
A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering—0
A Crowdsourcing Approach for Annotating Causal Relation Instances in Wikipedia—0
Exploring and Analyzing Machine Commonsense Benchmarks—0
Enhancing Pipeline-Based Conversational Agents with Large Language Models—0
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—0
Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens—0
Answer Presentation in Question Answering over Linked Data using Typed Dependency Subtree Patterns—0
Bridging the Training-Inference Gap for Dense Phrase Retrieval—0
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets—0
A Graph-Based Approach to String Regeneration—0
Bridging the Preference Gap between Retrievers and LLMs—0
Bridging the Language Gap: Knowledge Injected Multilingual Question Answering—0
Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering—0
Bridging the Knowledge Gap: Enhancing Question Answering with World and Domain Knowledge—0
AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings—0
Abacus: A Cost-Based Optimizer for Semantic Operator Systems—0
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
Bridging the Gap: Deciphering Tabular Data Using Large Language Model—0
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling—0
Answer Interaction in Non-factoid Question Answering Systems—0
AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce—0
Answering Yes/No Questions via Question Inversion—0
Bridging Technology and Humanities: Evaluating the Impact of Large Language Models on Social Sciences Research with DeepSeek-R1—0
Answering Yes-No Questions by Penalty Scoring in History Subjects of University Entrance Examinations—0
AAD-LLM: Neural Attention-Driven Auditory Scene Understanding—0
Enhancing Multimodal LLM for Detailed and Accurate Video Captioning using Multi-Round Preference Optimization—0
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR—0
Bridging Question Answering and Discourse The case of Multi-Sentence Questions—0
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction—0
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions—0
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension—0
AGQA 2.0: An Updated Benchmark for Compositional Spatio-Temporal Reasoning—0
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding—0
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval—0
A Glimpse in ChatGPT Capabilities and its impact for AI research—0
Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering—0
Answering Unanswered Questions through Semantic Reformulations in Spoken QA—0
A criterion for Artificial General Intelligence: hypothetic-deductive reasoning, tested on ChatGPT—0
Enhancing Multi-Image Question Answering via Submodular Subset Selection—0
Bridge the Gap between Language models and Tabular Understanding—0
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering—0
Answering Science Exam Questions Using Query Reformulation with Background Knowledge—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