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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/DataScienceUIBK/TempAmbiQA
공식 코드https://github.com/DevSinghSachan/emdr2
공식 코드End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
https://github.com/DevSinghSachan/unsupervised-passage-reranking
공식 코드Improving Passage Retrieval with Zero-Shot Question Generation
https://github.com/Fantabulous-J/FSMODQA
공식 코드https://github.com/HLTCHKUST/CAiRE-COVID
공식 코드https://github.com/JasonForJoy/BRIEF
공식 코드BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
https://github.com/KNOT-FIT-BUT/R2-D2
공식 코드Pruning the Index Contents for Memory Efficient Open-Domain QA
https://github.com/MexicanLemonade/LLM-Misinfo-QA
공식 코드On the Risk of Misinformation Pollution with Large Language Models
https://github.com/NVIDIA/Megatron-LM
공식 코드End-to-End Training of Neural Retrievers for Open-Domain Question Answering
https://github.com/OceanskySun/GraftNet
공식 코드Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
https://github.com/U-Alberta/QA-partial-marks
공식 코드Accurate and Nuanced Open-QA Evaluation Through Textual Entailment
https://github.com/VodLM/vod
공식 코드https://github.com/YihongT/ITINERA
공식 코드https://github.com/abdoelsayed2016/grg
공식 코드Generator-Retriever-Generator Approach for Open-Domain Question Answering
https://github.com/alibaba-nlp/vec-ra-odqa
공식 코드Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
https://github.com/alibaba-nlp/webwalker
공식 코드https://github.com/alirezasalemi7/urag
공식 코드https://github.com/allenai/bi-att-flow
공식 코드https://github.com/allenai/semantic_fragments
공식 코드What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
https://github.com/aloneirew/coresearch
공식 코드Cross-document Event Coreference Search: Task, Dataset and Modeling
https://github.com/amaiya/ktrain
공식 코드https://github.com/amazon-science/contextualunderstanding-contrastivedecoding
공식 코드Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding
https://github.com/amzn/refuel-open-domain-qa
공식 코드Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
https://github.com/apple/ml-mkqa
공식 코드MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
https://github.com/apple/ml-qrecc
공식 코드Open-Domain Question Answering Goes Conversational via Question Rewriting
https://github.com/awslabs/durepa-hybrid-qa
공식 코드Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
https://github.com/bdhingra/ga-reader
공식 코드https://github.com/beerqa/irrr
공식 코드Answering Open-Domain Questions of Varying Reasoning Steps from Text
https://github.com/carriex/recomp
공식 코드RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation
https://github.com/castorini/pyserini
공식 코드https://github.com/chanliang/conner
공식 코드Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
https://github.com/clovaai/minimal-rnr-qa
공식 코드Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering
https://github.com/datascienceuibk/arabicaqa
공식 코드ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
https://github.com/datascienceuibk/hintqa
공식 코드Exploring Hint Generation Approaches in Open-Domain Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.