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Logical Fallacies

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코드

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

https://github.com/Amir-pyh/LOGICOM

공식 코드

How susceptible are LLMs to Logical Fallacies?

https://github.com/LibrAIResearch/ruozhibench

공식 코드

RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises

https://github.com/UKPLab/arxiv2024-missciplus

공식 코드

Grounding Fallacies Misrepresenting Scientific Publications in Evidence

https://github.com/alessiopittiglio/mm-argfallacy

공식 코드

Leveraging Context for Multimodal Fallacy Classification in Political Debates

https://github.com/causalnlp/logical-fallacy

공식 코드

Logical Fallacy Detection

https://github.com/jw9603/logical_fallacy

공식 코드

Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation

https://github.com/ltroin/Smartybench

공식 코드

Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles

https://github.com/lucamouchel/Logical-Fallacies

공식 코드

A Logical Fallacy-Informed Framework for Argument Generation

https://github.com/openbmb/olympiadbench

공식 코드

OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

https://github.com/raising-hrx/fallacies

공식 코드

A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning

https://github.com/usc-isi-i2/logical-fallacy-identification

공식 코드

Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments

https://github.com/yuanyuanlei-nlp/logical_fallacy_emnlp_2024

공식 코드

Boosting Logical Fallacy Reasoning in LLMs via Logical Structure Tree

https://github.com/zhpinkman/cbr

공식 코드

Case-Based Reasoning with Language Models for Classification of Logical Fallacies

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/rvlopes/gloria

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

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