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 8651–8700 of 10817 papers

TitleStatusHype
Federated Retrieval Augmented Generation for Multi-Product Question Answering—0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks—0
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning—0
Fermi at SemEval-2019 Task 8: An elementary but effective approach to Question Discernment in Community QA Forums—0
FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Few-shot Multi-hop Question Answering over Knowledge Base—0
Few-shot Multimodal Multitask Multilingual Learning—0
Few-shot Policy (de)composition in Conversational Question Answering—0
Few-shot Query-Focused Summarization with Prefix-Merging—0
Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems—0
Few-shot Unified Question Answering: Tuning Models or Prompts?—0
Few-Shot VQA with Frozen LLMs: A Tale of Two Approaches—0
FFA Sora, video generation as fundus fluorescein angiography simulator—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation—0
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference—0
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering—0
Filling a Knowledge Graph with a Crowd—0
Fill-in-the-Blank: A Challenging Video Understanding Evaluation Framework—0
FilterRAG: Zero-Shot Informed Retrieval-Augmented Generation to Mitigate Hallucinations in VQA—0
Financial Knowledge Large Language Model—0
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation—0
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering—0
Finding Generalizable Evidence by Learning to Convince Q\&A Models—0
Finding Optimal 1-Endpoint-Crossing Trees—0
Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning—0
Finding Similar Medical Questions from Question Answering Websites—0
Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora—0
Findings on Conversation Disentanglement—0
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering—0
Finding the Middle Ground - A Model for Planning Satisficing Answers—0
Finding What Matters in Questions—0
Find The Gap: Knowledge Base Reasoning For Visual Question Answering—0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity—0
Fine-Grained Discourse Structures in Continuation Semantics—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Fine-grained Semantic Textual Similarity for Serbian—0
Fine-grained Structure-based News Genre Categorization—0
Finely Tuned, 2 Billion Token Based Word Embeddings for Portuguese—0
FINET: Context-Aware Fine-Grained Named Entity Typing—0
Fine-tune BERT with Sparse Self-Attention Mechanism—0
Fine-tuning and aligning question answering models for complex information extraction tasks—0
Finetuning Language Models to Emit Linguistic Expressions of Uncertainty—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets—0
Fine-tuning Large Language Models with Sequential Instructions—0
Fine-Tuning LLMs for Reliable Medical Question-Answering Services—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