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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/vodolan/DenotationIdentification
공식 코드Denotation Extraction for Interactive Learning in Dialogue Systems
https://github.com/voidism/ear
공식 코드Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
https://github.com/voidism/selfcite
공식 코드SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
https://github.com/voidmagic/parameter-differentiation
공식 코드Parameter Differentiation based Multilingual Neural Machine Translation
https://github.com/vqaonline/vqaonlinevisualization
공식 코드Fully Authentic Visual Question Answering Dataset from Online Communities
https://github.com/vru-next/videoqa
공식 코드Video Question Answering: Datasets, Algorithms and Challenges
https://github.com/vt-nlp/socratic-questioning
공식 코드The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
https://github.com/vuhoangminh/vqa_medical
공식 코드A Question-Centric Model for Visual Question Answering in Medical Imaging
https://github.com/vul337/callee
공식 코드Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning
https://github.com/wabyking/qnn
공식 코드https://github.com/wad3birch/AutoBench-V
공식 코드AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
https://github.com/waetr/KET-RAG
공식 코드KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG
https://github.com/waithz/gw-moe
공식 코드GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
https://github.com/waitxian/hicd
공식 코드https://github.com/waltonfuture/InstructionGPT-4
공식 코드InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
https://github.com/wangcongcong123/ttt
공식 코드https://github.com/wangcunxiang/Can-PLM-Serve-as-KB-for-CBQA
공식 코드Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?
https://github.com/wangcunxiang/graph-as-tokens
공식 코드Exploiting Abstract Meaning Representation for Open-Domain Question Answering
https://github.com/wangcunxiang/qa-eval
공식 코드https://github.com/wangcunxiang/rfid
공식 코드RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
https://github.com/wangjk666/omnivid
공식 코드OmniVid: A Generative Framework for Universal Video Understanding
https://github.com/wangjw424/commonsenseqa-art
공식 코드ArT: All-round Thinker for Unsupervised Commonsense Question-Answering
https://github.com/wangpf3/Commonsense-Path-Generator
공식 코드Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
https://github.com/wangpinggl/TREQS
공식 코드Text-to-SQL Generation for Question Answering on Electronic Medical Records
https://github.com/wangskygit/passage-sieve
공식 코드https://github.com/wangsygit/stepwiseqa
공식 코드Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering
https://github.com/wangtianyiftd/dialogue
공식 코드Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning
https://github.com/wangtianyiftd/dialogue_pretrain
공식 코드Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning
https://github.com/wangweisjtu/otur
공식 코드https://github.com/wangywust/deepedit
공식 코드https://github.com/wangywust/primacyeffectgpt
공식 코드https://github.com/wangzheallen/STL-VQA
공식 코드Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
https://github.com/wanliyoung/revisit-editing-evaluation
공식 코드The Mirage of Model Editing: Revisiting Evaluation in the Wild
https://github.com/wasiahmad/PolicyQA
공식 코드PolicyQA: A Reading Comprehension Dataset for Privacy Policies
https://github.com/wasiahmad/Syntax-MBERT
공식 코드Syntax-augmented Multilingual BERT for Cross-lingual Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.