https://github.com/Abhiram4572/mi_bart
공식 코드Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering
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Answer Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Aishwarya-NR/LRTA_Perturbed_Dataset
공식 코드https://github.com/AkariAsai/CORA
공식 코드One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval
https://github.com/DevSinghSachan/emdr2
공식 코드End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
https://github.com/Georgetown-IR-Lab/GAuGE
공식 코드https://github.com/HITsz-TMG/SKURG
공식 코드https://github.com/HenryJunW/TAG
공식 코드TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation
https://github.com/HomeroRR/rmm
공식 코드https://github.com/HooRin/AuditWen
공식 코드https://github.com/RegNLP/RePASs
공식 코드RIRAG: Regulatory Information Retrieval and Answer Generation
https://github.com/SamyAteia/BioRAGent
공식 코드https://github.com/TheOpenSI/CoSMIC
공식 코드Unleashing Artificial Cognition: Integrating Multiple AI Systems
https://github.com/WHUIR/RAGE
공식 코드Review-Driven Answer Generation for Product-Related Questions in E-Commerce
https://github.com/WorkInTheDark/FairytaleQA_QAG_System
공식 코드It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
https://github.com/X1AOX1A/UniPoll
공식 코드UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization
https://github.com/ZJU-DAILY/MQA
공식 코드An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models
https://github.com/acade-mate/o2-searcher
공식 코드O$^2$-Searcher: A Searching-based Agent Model for Open-Domain Open-Ended Question Answering
https://github.com/aizanzafar/MedLogicAQA
공식 코드https://github.com/amazon-science/contextual-product-qa
공식 코드xPQA: Cross-Lingual Product Question Answering across 12 Languages
https://github.com/amazonqa/amazonqa
공식 코드https://github.com/anastasiakrith/multimodal-retrieval-for-vwsd
공식 코드Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation
https://github.com/anranwu-richpo/dcqa
공식 코드https://github.com/asahi417/lm-question-generation
공식 코드An Empirical Comparison of LM-based Question and Answer Generation Methods
https://github.com/bangliu/ACS-QG
공식 코드Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/bernardoleite/fairytaleqa-translated
공식 코드https://github.com/bernardoleite/few-shot-prompting-qg-control
공식 코드On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension
https://github.com/buaadreamer/easyrag
공식 코드EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
https://github.com/chenyiqun/mmoa-rag
공식 코드Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
https://github.com/cxcscmu/ragviz
공식 코드RAGViz: Diagnose and Visualize Retrieval-Augmented Generation
https://github.com/czy1999/synthrag
공식 코드An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms
https://github.com/declare-lab/CICERO
공식 코드CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
https://github.com/dengyang17/OAAG
공식 코드Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
https://github.com/dengyang17/tois-page
공식 코드Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling
https://github.com/endrikacupaj/VOGUE
공식 코드https://github.com/ethanleo-lyx/llmqa
공식 코드Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
https://github.com/fantabulous-j/class
공식 코드Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.