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Open-Domain Question Answering
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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/google-research/tapas
공식 코드Open Domain Question Answering over Tables via Dense Retrieval
https://github.com/google/trax/tree/master/trax/models/reformer
공식 코드https://github.com/gorov/BookQA
공식 코드Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
https://github.com/gsidiropoulos/dense-retrieval-against-misspellings
공식 코드Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings
https://github.com/gsidiropoulos/hybrid_retrieval_for_efficient_qa
공식 코드Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering
https://github.com/henryzhao5852/distdr
공식 코드Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation
https://github.com/heydaari/CAG
공식 코드https://github.com/hkunlp/icl-ceil
공식 코드https://github.com/hplt-project/monolingual-multilingual-instruction-tuning
공식 코드Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
https://github.com/huggingface/transformers
공식 코드https://github.com/huggingface/transformers/tree/master/examples/research_projects/rag-end2end-retriever
공식 코드Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
https://github.com/husseinmozannar/SOQAL
공식 코드https://github.com/hyintell/retrievalqa
공식 코드https://github.com/ibm-ecosystem-engineering/blended-rag
공식 코드https://github.com/ibm/kgi-slot-filling
공식 코드https://github.com/ielab/typos-aware-bert
공식 코드Dealing with Typos for BERT-based Passage Retrieval and Ranking
https://github.com/is2ai/kazqad
공식 코드https://github.com/jeffhj/raven
공식 코드RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
https://github.com/jhyuklee/DensePhrases
공식 코드https://github.com/jhyuklee/sparc
공식 코드Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
https://github.com/jind11/MedQA
공식 코드https://github.com/jingtaozhan/repconc
공식 코드https://github.com/jjzha/fs1
공식 코드Scaling Reasoning can Improve Factuality in Large Language Models
https://github.com/jmnian/weak_label_for_rag
공식 코드W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
https://github.com/jmnian/wrag
공식 코드W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
https://github.com/jumxglhf/grape
공식 코드Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
https://github.com/jun-jie-huang/otter
공식 코드Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
https://github.com/jzbjyb/reatt
공식 코드Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
https://github.com/jzhoubu/hlp
공식 코드Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
https://github.com/jzhoubu/vdr
공식 코드https://github.com/kkskp/re-rag
공식 코드https://github.com/kmswin1/qpaug
공식 코드QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs
https://github.com/ldilab/ecorag
공식 코드ECoRAG: Evidentiality-guided Compression for Long Context RAG
https://github.com/lezhang7/moqagpt
공식 코드MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
https://github.com/likicode/QA-generalize
공식 코드Challenges in Generalization in Open Domain Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.