Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929381026299904 |
|---|---|
| author | Koka, Sahas Vuong, Anthony Kataria, Anish |
| author_facet | Koka, Sahas Vuong, Anthony Kataria, Anish |
| contents | In an era increasingly influenced by artificial intelligence, the detection of fake news is crucial, especially in contexts like election seasons where misinformation can have significant societal impacts. This study evaluates the effectiveness of various LLMs in identifying and filtering fake news content. Utilizing a comparative analysis approach, we tested four large LLMs -- GPT-4, Claude 3 Sonnet, Gemini Pro 1.0, and Mistral Large -- and two smaller LLMs -- Gemma 7B and Mistral 7B. By using fake news dataset samples from Kaggle, this research not only sheds light on the current capabilities and limitations of LLMs in fake news detection but also discusses the implications for developers and policymakers in enhancing AI-driven informational integrity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06584 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis Koka, Sahas Vuong, Anthony Kataria, Anish Computation and Language Artificial Intelligence In an era increasingly influenced by artificial intelligence, the detection of fake news is crucial, especially in contexts like election seasons where misinformation can have significant societal impacts. This study evaluates the effectiveness of various LLMs in identifying and filtering fake news content. Utilizing a comparative analysis approach, we tested four large LLMs -- GPT-4, Claude 3 Sonnet, Gemini Pro 1.0, and Mistral Large -- and two smaller LLMs -- Gemma 7B and Mistral 7B. By using fake news dataset samples from Kaggle, this research not only sheds light on the current capabilities and limitations of LLMs in fake news detection but also discusses the implications for developers and policymakers in enhancing AI-driven informational integrity. |
| title | Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.06584 |