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Formal Logic

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/albacl/klaus

공식 코드

Design of quantum optical experiments with logic artificial intelligence

https://github.com/azreasoners/gpt-asp-rules

공식 코드

Leveraging Large Language Models to Generate Answer Set Programs

https://github.com/cmu-pasta/onward24

공식 코드

Software Engineering Methods For AI-Driven Deductive Legal Reasoning

https://github.com/cse-ai-lab/RealCQA

공식 코드

RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic

https://github.com/dicelab-rhul/game-formaliser

공식 코드

Autoformalization of Game Descriptions using Large Language Models

https://github.com/dicelab-rhul/lelma

공식 코드

Towards Logically Sound Natural Language Reasoning with Logic-Enhanced Language Model Agents

https://github.com/gblackout/logicllama

공식 코드

Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

https://github.com/hitachi-nlp/fld

공식 코드

Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

https://github.com/kumarmanas/TAMP_COT_TL

공식 코드

CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning

https://github.com/larahack/formalization_papers_supplemental

공식 코드

Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers

https://github.com/princeton-vl/MetaQNL.jl

공식 코드

Learning Symbolic Rules for Reasoning in Quasi-Natural Language

https://github.com/princeton-vl/metaqnl

공식 코드

Learning Symbolic Rules for Reasoning in Quasi-Natural Language

https://github.com/psunlpgroup/fover

공식 코드

Training Step-Level Reasoning Verifiers with Formal Verification Tools

https://github.com/wufeiwuwoshihua/lexicaldiver

공식 코드

Are LLMs Reliable Translators of Logical Reasoning Across Lexically Diversified Contexts?

https://gitlab.uni-koblenz.de/obermaie/negationcognitivereasoning

공식 코드

Negation in Cognitive Reasoning

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/hitachi-nlp/fld-corpus

Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

https://github.com/hitachi-nlp/fld-generator

Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic

https://github.com/paolo7/shacl2fol

Satisfiability and Containment of Recursive SHACL

https://github.com/pwollstadt/IDTxl

Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/thejohncrafter/flows

Lectures on Jacques Herbrand as a Logician

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.