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Question Answering
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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/abachaa/MedQuAD
공식 코드https://github.com/abaheti95/QADialogSystem
공식 코드Fluent Response Generation for Conversational Question Answering
https://github.com/abdoelsayed2016/grg
공식 코드Generator-Retriever-Generator Approach for Open-Domain Question Answering
https://github.com/abdoelsayed2016/legal-question-answering-review
공식 코드https://github.com/abecode/emo20q-web
공식 코드Emotion Twenty Questions Dialog System for Lexical Emotional Intelligence
https://github.com/abhi1nandy2/emnlp-2021-findings
공식 코드https://github.com/abhi23run/cse8803_dlt_project
공식 코드https://github.com/abhijithneilabraham/tableqa
공식 코드https://github.com/abril4416/Mod-Zero-VQA
공식 코드https://github.com/abril4416/kgen_vqa
공식 코드https://github.com/abskjha/Barlow-constrained-VQA
공식 코드Barlow constrained optimization for Visual Question Answering
https://github.com/aburns4/MoTIF
공식 코드https://github.com/acade-mate/o2-searcher
공식 코드O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering
https://github.com/academiasinicanlplab/mvqg-dataset-of-generating-engaging-questions-for-multiple-images
공식 코드Multi-VQG: Generating Engaging Questions for Multiple Images
https://github.com/accuracy-disparity/at-on-ad
공식 코드Understanding the Impact of Adversarial Robustness on Accuracy Disparity
https://github.com/acharkq/prott3
공식 코드ProtT3: Protein-to-Text Generation for Text-based Protein Understanding
https://github.com/achaudhury/shmoop-corpus
공식 코드The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries
https://github.com/achernikova/cybersecurity_evasion
공식 코드FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments
https://github.com/acmi-lab/self-pretrain
공식 코드Downstream Datasets Make Surprisingly Good Pretraining Corpora
https://github.com/acmislab/pediabench
공식 코드PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Models
https://github.com/acolas1/TutorialVQAData
공식 코드https://github.com/acpotluri/lfqa_summary
공식 코드Concise Answers to Complex Questions: Summarization of Long-form Answers
https://github.com/activevisionlab/awesome-llm-3d
공식 코드https://github.com/adacheng/egothink
공식 코드EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models
https://github.com/adapter-hub/efficient-task-transfer
공식 코드https://github.com/adapter-hub/xgqa
공식 코드https://github.com/adaptivemotorcontrollab/llavaction
공식 코드LLaVAction: evaluating and training multi-modal large language models for action recognition
https://github.com/adelwang/kd-cot
공식 코드https://github.com/adishourya/medm
공식 코드Adapting Lightweight Vision Language Models for Radiological Visual Question Answering
https://github.com/aditya10/vlc-bert
공식 코드VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
https://github.com/adityaarunsinghal/Domain-Adaptation
공식 코드Task Transfer and Domain Adaptation for Zero-Shot Question Answering
https://github.com/adityakane2001/continual_vqa
공식 코드https://github.com/adityavavre/videgovlm
공식 코드EgoVLM: Policy Optimization for Egocentric Video Understanding
https://github.com/adlnlp/MultimodalLLM
공식 코드Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond
https://github.com/adlnlp/attention_vl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.