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Question Answering

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하위 태스크: 19
평가표: 153
보존된 평가 기록: 1,781

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 )

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MapQaTor/.github/tree/main/profile

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MapQaTor: An Extensible Framework for Efficient Annotation of Map-Based QA Datasets

https://github.com/MarcBS/VIBIKNet

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VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering

https://github.com/MatNLP/UniPSDA

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UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding

https://github.com/Mayer123/HyKAS-CSKG

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Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering

https://github.com/McGill-NLP/feedbackqa

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Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment

https://github.com/MedHK23/MedViLaM

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MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation

https://github.com/MedMCQA/MedMCQA

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MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering

https://github.com/MexicanLemonade/LLM-Misinfo-QA

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On the Risk of Misinformation Pollution with Large Language Models

https://github.com/Miaoranmmm/OPERA

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OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience

https://github.com/MichiganNLP/video-fill-in-the-blank

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FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework

https://github.com/Microsoft/SDNet

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SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering

https://github.com/Mingxiao-Li/DMMGR

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Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering

https://github.com/MiuLab/CQA-Study

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An Empirical Study of Content Understanding in Conversational Question Answering

https://github.com/MiuLab/E2EMathSolver

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Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

https://github.com/MiuLab/QAInfomax

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QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization

https://github.com/Moradnejad/Predicting-Subjective-Features-on-QA-Websites

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Predicting Subjective Features of Questions of QA Websites using BERT

https://github.com/Mr8ND/ACORE-LFI

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Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference

https://github.com/MrZihan/g3D-LF

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g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks

https://github.com/MuyeHuang/VProChart

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VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning

https://github.com/Mvrjustid/MHN-IJCAI22

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Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering

https://github.com/NExTplusplus/TAT-QA

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TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

https://github.com/NNReasoning/What-Can-Neural-Networks-Reason-About

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What Can Neural Networks Reason About?

https://github.com/NTMC-Community/MatchZoo

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MatchZoo: A Toolkit for Deep Text Matching

https://github.com/NTMC-Community/MatchZoo-Studio

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MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching

https://github.com/NVIDIA-AI-IOT/remembr

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ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation

https://github.com/NVIDIA/Megatron-LM

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Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

https://github.com/NVIDIA/NeMo

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BioMegatron: Larger Biomedical Domain Language Model

https://github.com/Nardien/NMG

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Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation

https://github.com/NavidRajabi/EMQA

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Towards More Equitable Question Answering Systems: How Much More Data Do You Need?

https://github.com/NeosKnight233/MuDAF

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MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention Heads

https://github.com/Neural-Space/indic-transformers

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Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages

https://github.com/Neutralzz/RefQA

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Harvesting and Refining Question-Answer Pairs for Unsupervised QA

https://github.com/Neutralzz/syntax_aware_local_attention

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Improving BERT with Syntax-aware Local Attention

https://github.com/Nickolas-option/QA_on_Tabular_Data_SemEval2025_Task8

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Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA

https://github.com/NickyFot/HitchhikersGuide

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A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning

https://github.com/Nouf-Alabbasi/oKUmura_AI_Telecom_challenge

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Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.