https://github.com/xinyuanlu00/tart
공식 코드TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning
Papers with Code 아카이브
Fact Verification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Fact verification, also called "fact checking", is a process of verifying facts in natural text against a database of facts.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yale-lily/reastap
공식 코드ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
https://github.com/yasumasaonoe/creak
공식 코드CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
https://github.com/yibing-du/adversarial-factcheck
공식 코드Synthetic Disinformation Attacks on Automated Fact Verification Systems
https://github.com/ysymyth/ReAct
공식 코드https://github.com/yuh-zha/alignscore
공식 코드AlignScore: Evaluating Factual Consistency with a Unified Alignment Function
https://github.com/yuta-mukobara/rlf-kgat
공식 코드https://github.com/zcccccz/causalwalk
공식 코드Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment
https://github.com/znhy1024/protoco
공식 코드https://github.com/zorazrw/filco
공식 코드Learning to Filter Context for Retrieval-Augmented Generation
https://github.com/D-Roberts/multilingual-nli-ECIR2021
https://github.com/IKMLab/CFEVER-data
https://github.com/MindSpore-scientific-2/code-10/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-4/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-5/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/ShayekhBinIslam/openrag
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/ShyamSubramanian/HESM
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
https://github.com/StrombergNLP/danfever
https://github.com/Zefty/rag-end2end-retriever
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/amaiya/onprem
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/assafelovic/gpt-researcher
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/bangoc123/drop-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/bangoc123/retrieval-backend-with-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/billxbf/rewoo
https://github.com/cyqiq/multicot
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
https://github.com/declare-lab/sancl
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
https://github.com/deepset-ai/haystack
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/docarray/docarray
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/dujiaxin/semanticRetrievalMRS
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
https://github.com/facebookresearch/editeval
https://github.com/fate-ubw/raglab
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/google-research/chain-of-table
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
https://github.com/hkust-nlp/agentboard
https://github.com/huggingface/transformers
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.