Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
A Comparative Study of Ranking Formulas Based on Consistency
Authors: Badran Raddaoui, Christian Straßer, Said Jabbour
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,923 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,904 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | By means of the postulate-based and comparative study, this paper provides a first systematic study of different approaches to ranking formulas based on consistency considerations. In this paper, a framework for ranking semantics in propositional logic was presented, where formulas are ranked based on consistency by using solely the inherent structure of the knowledge base. |
| Researcher Affiliation | Academia | 1SAMOVAR, T el ecom Sud Paris, Institut Polytechnique de Paris, France 2Institute for Philosophy II, Ruhr University Bochum, Germany 3CRIL CNRS, Universit e d Artois, France |
| Pseudocode | No | The paper defines concepts and semantics mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses illustrative examples (e.g., Example 1, K1) to demonstrate concepts but does not utilize or refer to any publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets that require validation splits. No dataset split information is provided. |
| Hardware Specification | No | The paper describes a theoretical framework and does not report on empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper outlines a theoretical framework and does not report on empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper presents a theoretical framework and does not conduct empirical experiments, hence there are no details provided regarding experimental setup or hyperparameters. |