https://github.com/x-lance/neusym-rag
공식 코드NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/x-plug/mplug-docowl
공식 코드mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding
https://github.com/x-plug/mplug-owl
공식 코드mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
https://github.com/xdao85/vnhsge
공식 코드VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models
https://github.com/xfactlab/emnlp2023-damaging-retrieval
공식 코드https://github.com/xfactlab/emnlp2023-knowledge-corpus-error
공식 코드https://github.com/xiami2019/halluqa
공식 코드https://github.com/xiangyue9607/CliniRC
공식 코드Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
https://github.com/xiangyue9607/c-more
공식 코드C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References
https://github.com/xiangyue9607/qve
공식 코드Synthetic Question Value Estimation for Domain Adaptation of Question Answering
https://github.com/xiaojino/RUArt
공식 코드RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering
https://github.com/xiaojinying/SEVEN
공식 코드https://github.com/xiaomabufei/fgahoi
공식 코드FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection
https://github.com/xiaoman-zhang/PMC-VQA
공식 코드PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
https://github.com/xiaomeng-ma/tomchallenges
공식 코드ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind
https://github.com/xiaomi-research/r1-aqa
공식 코드Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering
https://github.com/xiaowu0162/longmemeval
공식 코드LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
https://github.com/xiaowu0162/visualize-then-retrieve
공식 코드https://github.com/xiaoxinhe/g-retriever
공식 코드G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
https://github.com/xid32/naacl_2025_twm
공식 코드Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
https://github.com/xijun-cs/vila
공식 코드ViLA: Efficient Video-Language Alignment for Video Question Answering
https://github.com/xikai97/med-mim
공식 코드Medical Large Vision Language Models with Multi-Image Visual Ability
https://github.com/xingdi-eric-yuan/imrc_public
공식 코드Interactive Machine Comprehension with Information Seeking Agents
https://github.com/xingdi-eric-yuan/qait_public
공식 코드https://github.com/xingruiwang/3d-aware-vqa
공식 코드3D-Aware Visual Question Answering about Parts, Poses and Occlusions
https://github.com/xinleihe/mgtbench
공식 코드https://github.com/xinyadu/eeqa
공식 코드https://github.com/xinyadu/rgqa
공식 코드Retrieval-Augmented Generative Question Answering for Event Argument Extraction
https://github.com/xinyuanlu00/qacheck
공식 코드QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking
https://github.com/xinyuanlu00/tart
공식 코드TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning
https://github.com/xinzhel/unlearnable_texts
공식 코드Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data
https://github.com/xitaso/lgar
공식 코드LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature Reviews
https://github.com/xiusic/gotta
공식 코드Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
https://github.com/xiuyuan-chen/autoeval-video
공식 코드https://github.com/xiye17/explselection
공식 코드Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting
https://github.com/xiye17/interpcalib
공식 코드Can Explanations Be Useful for Calibrating Black Box Models?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.