https://github.com/DancingSoul/NQ_BERT-DM
공식 코드Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IBM/span-selection-pretraining
공식 코드https://github.com/McGill-NLP/MLQuestions
공식 코드https://github.com/NVIDIA/Megatron-LM
공식 코드End-to-End Training of Neural Retrievers for Open-Domain Question Answering
https://github.com/agrzheng/GRADA
공식 코드GRADA: Graph-based Reranker against Adversarial Documents Attack
https://github.com/alexa/wqa_tanda
공식 코드TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
https://github.com/apple/ml-mkqa
공식 코드MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
https://github.com/aritra23/dragonverseqa
공식 코드DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answering
https://github.com/ark-130994/gw-solvers
공식 코드Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem
https://github.com/baidu/DuReader
공식 코드https://github.com/bashlab/llasa
공식 코드LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors
https://github.com/cmavro/gnn-rag
공식 코드GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
https://github.com/cmavro/tempoqr
공식 코드https://github.com/craymichael/unfooling
공식 코드https://github.com/dongxiangjue/closed-book-question-generation-via-contrastive-learning
공식 코드https://github.com/edward-sun/recite
공식 코드https://github.com/ellaneeman/disent_qa
공식 코드DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
https://github.com/eunseongc/mgfid
공식 코드https://github.com/facebookresearch/UnsupervisedQA
공식 코드https://github.com/facebookresearch/atlas
공식 코드Atlas: Few-shot Learning with Retrieval Augmented Language Models
https://github.com/facebookresearch/layerskip
공식 코드LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
https://github.com/fantabulous-j/class
공식 코드Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision
https://github.com/fuzihaofzh/analyzeparameterefficientfinetune
공식 코드https://github.com/google-research-datasets/QED
공식 코드QED: A Framework and Dataset for Explanations in Question Answering
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/language
공식 코드https://github.com/google-research/tapas
공식 코드Open Domain Question Answering over Tables via Dense Retrieval
https://github.com/hkust-nlp/dart-math
공식 코드DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
https://github.com/hugozhl/mevi
공식 코드https://github.com/iai-group/iqn-kgqa
공식 코드https://github.com/ielab/asyncval
공식 코드Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training
https://github.com/ielab/denseretriever-knowledge-acquisition
공식 코드Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
https://github.com/is2ai/kazqad
공식 코드https://github.com/kidist-amde/ddro
공식 코드Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
https://github.com/kidist-amde/ddro-direct-document-relevance-optimization
공식 코드Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
https://github.com/kkskp/re-rag
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.