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Answer Generation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ruc-gsai/yulan-ir
공식 코드RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
https://github.com/salesforce/VD-BERT
공식 코드https://github.com/samyateia/bioasq
공식 코드https://github.com/scai-conf/scai-qrecc-21
공식 코드https://github.com/sding11/RAG-VR
공식 코드RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR Environments
https://github.com/shabnam-b/elqa
공식 코드ELQA: A Corpus of Metalinguistic Questions and Answers about English
https://github.com/shuohangwang/mprc
공식 코드R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
https://github.com/sleeepeer/poisonedrag
공식 코드PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
https://github.com/szhang42/Calibration_qa
공식 코드Knowing More About Questions Can Help: Improving Calibration in Question Answering
https://github.com/thinklab-sjtu/awesome-llm4eda
공식 코드LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation
https://github.com/thunlp/fastfid
공식 코드FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
https://github.com/tobischimanski/climretrieve
공식 코드ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures
https://github.com/uiucnlp/mumuqa
공식 코드https://github.com/ukplab/peerqa
공식 코드PeerQA: A Scientific Question Answering Dataset from Peer Reviews
https://github.com/umanlp/zusammenqa
공식 코드https://github.com/unitn-sml/physician-medLLM-interaction
공식 코드Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
https://github.com/vectara/mirage-bench
공식 코드MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems
https://github.com/visual-text-QA/VTQA-Demo
공식 코드VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning
https://github.com/wangskygit/passage-sieve
공식 코드https://github.com/wangzc1233/Prot2Chat
공식 코드Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure
https://github.com/wendywangwwt/zero-shot-complex-question-answering-on-long-scientific-documents
공식 코드Zero-Shot Complex Question-Answering on Long Scientific Documents
https://github.com/wentaotan/sena
공식 코드Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-Evolution
https://github.com/wlzhang2020/reasonrag
공식 코드Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
https://github.com/yfyuan01/mcpqa
공식 코드Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering
https://github.com/yuqi-zhou/gui-g1
공식 코드GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents
https://github.com/yxh-y/tablerag
공식 코드TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
https://github.com/yzhjessica/cdvqa
공식 코드https://github.com/zeaver/multifactor
공식 코드Improving Question Generation with Multi-level Content Planning
https://github.com/zhukun1020/noisefilter_ib
공식 코드https://github.com/zihaow123/unimm
공식 코드Unified Multimodal Model with Unlikelihood Training for Visual Dialog
https://github.com/zjukg/trustuqa
공식 코드TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ArvinZhuang/BiTAG
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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