Logical Fallacies

7 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

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

allenai/dolma NA 2021

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.

Logical Fallacy Detection

causalnlp/logical-fallacy 28 Feb 2022

In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).

Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments

usc-isi-i2/logical-fallacy-identification 12 Dec 2022

Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed.

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

zhpinkman/cbr 27 Jan 2023

The ease and speed of spreading misinformation and propaganda on the Web motivate the need to develop trustworthy technology for detecting fallacies in natural language arguments.

How susceptible are LLMs to Logical Fallacies?

Amir-pyh/LOGICOM 18 Aug 2023

Then, it evaluates the debater's performance in logical reasoning by contrasting the scenario where the persuader employs logical fallacies against one where logical reasoning is used.

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

raising-hrx/fallacies 14 Nov 2023

In this paper, we take a closer look at the self-verification abilities of LLMs in the context of logical reasoning, focusing on their ability to identify logical fallacies accurately.

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

openbmb/olympiadbench 21 Feb 2024

Notably, the best-performing model, GPT-4V, attains an average score of 17. 23% on OlympiadBench, with a mere 11. 28% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning.