https://github.com/noviscl/answerequiv
공식 코드What's in a Name? Answer Equivalence For Open-Domain Question Answering
Papers with Code 아카이브
TriviaQA
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nusnlp/amanda
공식 코드A Question-Focused Multi-Factor Attention Network for Question Answering
https://github.com/rajarshd/Multi-Step-Reasoning
공식 코드Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
https://github.com/shmsw25/qa-hard-em
공식 코드A Discrete Hard EM Approach for Weakly Supervised Question Answering
https://github.com/shuohangwang/mprc
공식 코드Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
https://github.com/stanford-futuredata/ColBERT
공식 코드https://github.com/stanfordnlp/ColBERT-QA
공식 코드https://github.com/studio-ousia/bpr
공식 코드Efficient Passage Retrieval with Hashing for Open-domain Question Answering
https://github.com/thunlp/fastfid
공식 코드FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
https://github.com/ustc-starteam/genki
공식 코드https://github.com/wangcunxiang/graph-as-tokens
공식 코드Exploiting Abstract Meaning Representation for Open-Domain Question Answering
https://github.com/wangcunxiang/rfid
공식 코드RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
https://github.com/2023-MindSpore-1/ms-code-155
https://github.com/2023-MindSpore-1/ms-code-161
https://github.com/AIResearchHub/transformergallery
https://github.com/DevSinghSachan/unsupervised-passage-reranking
End-to-End Training of Neural Retrievers for Open-Domain Question Answering
https://github.com/FenQQQ/Fusion-in-decoder
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/IBM/ColBERT-practical
https://github.com/Phrase-in-Context/eval
https://github.com/SatyamSoni23/Smart-Question-Answering-System-on-Document
Efficient and Robust Question Answering from Minimal Context over Documents
https://github.com/ZIZUN/MAFiD
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/a-rios/ats-models
https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/amazon-science/efficient-longdoc-classification
https://github.com/amoramine/Pegasus_Longformer_summarization
https://github.com/amoramine/Pegasus_with_Longformer_summarization
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/chrisc36/debias
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
https://github.com/erolm-a/colla-framework
Entities as Experts: Sparse Memory Access with Entity Supervision
https://github.com/esteng/persuasion_balanced_training
LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models
https://github.com/facebookresearch/DPR
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/facebookresearch/FiD
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/facebookresearch/reconsider
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
https://github.com/facebookresearch/xformers
https://github.com/han-shi/SparseBERT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.