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Antoniou, G.; Batsakis, S.: Defeasible Reasoning with Large Language Models - Initial Experiments and Future Directions. In: Vanthienen, Jan; Kliegr, Tomáš; Fodor, Paul; Lanti, Davide; Arndt, Dörthe; Kostylev, Egor V.; Mitsikas, Theodoros; Soylu, Ahmet (Eds.): RuleML+RR-Companion 2023: RuleML+RR Challenge and Doctoral Consortium 2023 : proceedings of the 17th International Rule Challenge and 7th Doctoral Consortium @ RuleML+RR 2023 co-located with 19th Reasoning Web Summer School (RW 2023) and 15th DecisionCAMP 2023 as part of Declarative AI 2023. Aachen, Germany : RWTH Aachen, 2023 (CEUR workshop proceedings ; 3485), 7687.
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Sum total of downloads: 10
| Abstract: | |
As Large Language Models gain prominence in the AI landscape, it is essential to understand their capabilities and limitations, among others in terms of reasoning. This paper is a first step towards understanding the capabilities in terms of defeasible rule-based reasoning. It presents results of initial experiments and discussed future research directions. | |
| License of this version: | CC BY 4.0 Unported |
| Document Type: | BookPart |
| Publishing status: | publishedVersion |
| Issue Date: | 2023 |
| Appears in Collections: | Forschungszentren |
distribution of downloads over the selected time period:
downloads by country:
| pos. | country | downloads | ||
|---|---|---|---|---|
| total | perc. | |||
| 1 |  |
Germany | 7 | 70.00% |
| 2 |  |
United States | 2 | 20.00% |
| 3 |  |
China | 1 | 10.00% |
Further download figures and rankings:
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