Papers with Code 아카이브
Open-Domain Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 109
Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mandarjoshi90/coref
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/martiansideofthemoon/relic-retrieval
https://github.com/mcao610/Factual-Error-Correction
https://github.com/meetyou-ai-lab/can-mc-evaluate-llms
https://github.com/mia-workshop/mia-shared-task-2022
Latent Retrieval for Weakly Supervised Open Domain Question Answering
https://github.com/microsoft/Table-Pretraining
https://github.com/microsoft/fastseq
https://github.com/minstar/BIDAF
https://github.com/mistralai/mistral-src
https://github.com/moutaigua8183/BookQA
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
https://github.com/naver/splade
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
https://github.com/nidhikamal-emb/DPR_repo
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/nlmatics/llmsherpa
https://github.com/nuochenpku/orca
Open-Domain Question Answering Goes Conversational via Question Rewriting
https://github.com/nyu-dl/dl4ir-searchQA
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
https://github.com/okanvk/Question-Answering-Project
Latent Retrieval for Weakly Supervised Open Domain Question Answering
https://github.com/open-vision-language/infoseek
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
https://github.com/opendatalab/charm
https://github.com/openmatch/ance-tele
https://github.com/phbst/tinyrag
Learning to Filter Context for Retrieval-Augmented Generation
https://github.com/prajjwal1/adaptive_transformer
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/prajjwal1/fluence
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/princeton-nlp/SimCSE
https://github.com/princeton-nlp/helmet
https://github.com/priyamtejaswin/multistep-retrieve-summarize
https://github.com/ptillet/torch-blocksparse
https://github.com/pytorch/fairseq
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/qywu/memformers
https://github.com/rajatgermany/qa-nlp
https://github.com/ruc-nlpir/flashrag
Making Retrieval-Augmented Language Models Robust to Irrelevant Context
https://github.com/sachit-n/drqa_doc_reader
https://github.com/samsam3232/qampari
https://github.com/shark-nlp/openicl
https://github.com/shijx12/kqapro_baselines
https://github.com/shmsw25/AmbigQA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.