miscellaneous-2

Logical Fallacies

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

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

A. Mensch, Katie Millican, Roman Ring, Eliza Rutherford, Jacob Menick, Sebastian Borgeaud, Aida Nematzadeh, O. Vinyals, K. Simonyan, Yujia Li, Igor Babuschkin, Po-Sen Huang, Johannes Welbl, K. Kavukcuoglu, D. Hassabis, James Bradbury, Chris Dyer, L. Sifre, George van den Driessche, Erich Elsen, D. Budden, G. Irving, Laura Weidinger, Maribeth Rauh, Iason Gabriel, William S. Isaac, Saffron Huang, Lisa Anne Hendricks, I. Higgins, Diego de Las Casas, Jack W. Rae, Siddhant M. Jayakumar, Xiang Lorraine Li, Francis Song, Cyprien de Masson d'Autume, Amy Wu, Angeliki Lazaridou, A. Kuncoro, Michela Paganini, Trevor Cai, Jordan Hoffmann, John Aslanides, Sarah Henderson, Susannah Young, Tom Hennigan, Albin Cassirer, Richard Powell, Amelia Glaese, Sumanth Dathathri, Jonathan Uesato, John F. J. Mellor, Antonia Creswell, Nat McAleese, Elena Buchatskaya, Esme Sutherland, Lena Martens, E. Gribovskaya, Domenic Donato, Jean-Baptiste Lespiau, M. Tsimpoukelli, N. Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Tobias Pohlen, Z. Gong, Daniel Toyama, Tayfun Terzi, Vladimir Mikulik, Aidan Clark, Aurelia Guy, Chris Jones, Matthew G. Johnson, Blake A. Hechtman, Edward Lockhart, Simon Osindero, Laura Rimell, Kareem W. Ayoub, J. Stanway, L. Bennett

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Tue Dec 07 2021

This paper presents an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher.

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Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection

Mrinmaya Sachan, Zhijing Jin, Abhinav Lalwani, Tasha Kim, Lovish Chopra, Christopher Hahn•Wed Apr 17 2024

This paper introduces Natural Language to First-Order Logic (NL2FOL), a framework to autoformalize natural language to FOL step by step using Large Language Models (LLMs), and uses Satisfiability Modulo Theory solvers to reason about the logical validity of natural language statements.

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Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments

Filip Ilievski, Zhivar Sourati, Hông-Ân Sandlin, Alain Mermoud, Vishnu Priya Prasanna Venkatesh, D. Deshpande, Himanshu Rawlani•Sun Dec 11 2022

This paper formalizes prior theoretical work on logical fallacies into a comprehensive three-stage evaluation framework of detection, coarse- grained, and fine-grained classification, and employs three families of robust and explainable methods based on prototype reasoning, instance-based reasoning, and knowledge injection.

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

Filip Ilievski, Zhivar Sourati, Hông-Ân Sandlin, Alain Mermoud•Thu Jan 26 2023

A Case-Based Reasoning method that classifies new cases of logical fallacy by language-modeling-driven retrieval and adaptation of historical cases, and designs four complementary strategies to enrich input representation for this model, based on external information about goals, explanations, counterarguments, and argument structure.

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How Susceptible Are LLMs to Logical Fallacies?

Xuesu Xiao, Amirreza Payandeh, Dan Pluth, Jordan Hosier, V. Gurbani•Thu Aug 17 2023

Findings indicate that both GPT-3.5 and GPT-4 can adjust their opinion through reasoning, however, when presented with logical fallacies, GPT-3.5 and GPT-4 are erroneously convinced 41% and 69% more often, respectively, compared to when logical reasoning is used.

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

Hongming Zhang, Dong Yu, Ruixin Hong, Xinyu Pang, Changshui Zhang•Mon Nov 13 2023

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, suggests that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self- Verification methods.

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

Maosong Sun, Xu Han, Zhiyuan Liu, Shengding Hu, Jinyi Hu, Chaoqun He, Renjie Luo, Yuzhuo Bai, Z. Thai, Junhao Shen, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi•Tue Feb 20 2024

This work presents OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark, featuring 8,476 problems from Olympiad-level mathematics and physics competitions, including the Chinese college entrance exam, and implements a comprehensive assessment methodology to accurately evaluate model responses.

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