https://github.com/PolyU-ChenLab/ETBench
공식 코드E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/PongNJ/V-RoAst
공식 코드V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?
https://github.com/Q-Future/Q-Bench
공식 코드https://github.com/QinJinghui/SAU-Solver
공식 코드Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems
https://github.com/RUCAIBox/Awesome-KBQA
공식 코드https://github.com/RUCAIBox/TextBox
공식 코드TextBox 2.0: A Text Generation Library with Pre-trained Language Models
https://github.com/Raising-hrx/FAME
공식 코드https://github.com/Raising-hrx/MetGen
공식 코드METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation
https://github.com/RaptorMai/online-continual-learning
공식 코드Online Class-Incremental Continual Learning with Adversarial Shapley Value
https://github.com/Rcrossmeister/RLQG
공식 코드Towards Better Question Generation in QA-based Event Extraction
https://github.com/RegNLP/RePASs
공식 코드RIRAG: Regulatory Information Retrieval and Answer Generation
https://github.com/RichardHGL/WSDM2021_NSM
공식 코드Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals
https://github.com/RujingYao/LSIM
공식 코드https://github.com/RuntianZ/doro
공식 코드https://github.com/RussianNLP/RussianSuperGLUE
공식 코드RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
https://github.com/RussianNLP/TAPE
공식 코드https://github.com/SDM-TIB/Falcon2.0
공식 코드Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
https://github.com/SDRMp/DRPG
공식 코드GUIDEQ: Framework for Guided Questioning for progressive informational collection and classification
https://github.com/SJTU-lqiu/QA4IE
공식 코드QA4IE: A Question Answering based Framework for Information Extraction
https://github.com/SJTUHaiyangYu/BackdoorMBTI
공식 코드BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation
https://github.com/SRI-CSL/TrinityMultimodalTrojAI
공식 코드https://github.com/SS47816/AGI-Elo
공식 코드https://github.com/SUTDCV/SUTD-TrafficQA
공식 코드https://github.com/SakanaAI/continuous-thought-machines
공식 코드https://github.com/SalesforceAIResearch/Unlocking-TextGen
공식 코드Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding
https://github.com/SamyAteia/BioRAGent
공식 코드https://github.com/SebiSebi/AI2-Reasoning-Challenge-ARC
공식 코드Improving Retrieval-Based Question Answering with Deep Inference Models
https://github.com/ServiceNow/repliqa
공식 코드RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
https://github.com/Seth-Park/MultimodalExplanations
공식 코드Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
https://github.com/SgfdDttt/sara
공식 코드A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
https://github.com/ShannonAI/dice_loss_for_NLP
공식 코드https://github.com/Shilun-Allan-Li/Ensemble-ALBERT-on-SQuAD-2.0
공식 코드https://github.com/SilongYong/SQA3D
공식 코드https://github.com/SinghJasdeep/Attention-on-Attention-for-VQA
공식 코드Attention on Attention: Architectures for Visual Question Answering (VQA)
https://github.com/SkelterLabsInc/JaQuAD
공식 코드JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
https://github.com/SkiddieAhn/Paper-AnyAnomaly
공식 코드AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.