Advancing Computational Humor: LLaMa-3 Based Generation with DistilBert Evaluation Framework
Humor generation presents significant challenges in the field of natural language processing, primarily due to its reliance on cultural backgrounds and subjective interpretations. These factors contribute to the variability of human-generated humor, necessitating computational models capable of mast...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03024.pdf |
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| Summary: | Humor generation presents significant challenges in the field of natural language processing, primarily due to its reliance on cultural backgrounds and subjective interpretations. These factors contribute to the variability of human-generated humor, necessitating computational models capable of mastering diverse comedic styles with minimal subjectivity and maximal generalizability. This study introduces a novel approach to humor generation by fine-tuning the LLaMA-3 language model with Low-Rank Adaptation (LoRA). The study developed a comprehensive dataset sourced from diverse online platforms, supplemented by non-humorous content from scientific literature and press conferences to enhance the model's discriminative capabilities. Utilizing DistilBERT for efficient evaluation, the fine-tuned LLaMA-3 achieved an impressive accuracy of 95.6% and an F1-score of 97.75%, surpassing larger models such as GPT-4o, and Gemini. These results demonstrate the model's exceptional capability in generating humor, offering a more efficient and scalable solution for applications such as conversational agents and entertainment platforms. This research advances the field by showcasing the benefits of comprehensive dataset preparation and targeted fine-tuning, providing a foundation for future developments in humor-related artificial intelligence applications. |
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| ISSN: | 2271-2097 |