ORPAILLEUR & SyNaLP at CLEF 2024 Task 2: Good Old Cross Validation for Large Language Models Yields the Best Humorous Detection - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2024

ORPAILLEUR & SyNaLP at CLEF 2024 Task 2: Good Old Cross Validation for Large Language Models Yields the Best Humorous Detection

Résumé

In the context of the JOKER 2024 Task 2 Challenge, this paper presents an emerging approach that leverages the latent representations derived from different Large Language Models (LLMs) to drive a classification mechanism. Our methodology involves exploiting the "knowledge" encoded in LLMs to effectively discriminate humor genres. Experimental results show promising results, demonstrating the effectiveness of our approach. However, inherent complexities remain, such as the proximity between certain classes and biases arising from the dataset distributions. These complexities warrant further investigation to refine the classification process and improve overall performance.
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hal-04696012 , version 1 (12-09-2024)

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  • HAL Id : hal-04696012 , version 1

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Pierre Epron, Gaël Guibon, Miguel Couceiro. ORPAILLEUR & SyNaLP at CLEF 2024 Task 2: Good Old Cross Validation for Large Language Models Yields the Best Humorous Detection. Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024), Sep 2024, Grenoble, France. pp.1841-1856. ⟨hal-04696012⟩
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