| _version_ | 1866902245533024256 |
|---|---|
| author | Álvarez-Cascos, Annunziata Rehm, Florian |
| author_facet | Álvarez-Cascos, Annunziata Rehm, Florian |
| contents | <p>This project explores the potential of AI-powered automation in the peer review process for CMS experiment publications at CERN, using the LLaMA 3.1 language model. The research initially employed a fine-tuning approach through Parameter-Efficient Fine-Tuning (PEFT), which, while effective in automating certain aspects such as ensuring consistency with formatting and style guidelines, required significant resources and time. To address these limitations, a prompt engineering approach was later adopted, significantly reducing the time required while improving the accuracy and consistency of reviews. By embedding a summary of CMS internal publication guidelines directly within the prompts, the model was able to perform without additional task-specific training. Both approaches demonstrated the potential for AI to streamline the peer review process, with the prompt engineering method showing particular promise in efficiency. The findings lay a strong foundation for future improvements, including further customization of training processes, dataset expansion, and the development of a graphical user interface (GUI), while also contributing valuable insights into the current capabilities and limitations of AI models in specialized domains.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_13856034 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A CMS AI to Improve Paper Publishing Álvarez-Cascos, Annunziata Rehm, Florian CERN openlab summer student project <p>This project explores the potential of AI-powered automation in the peer review process for CMS experiment publications at CERN, using the LLaMA 3.1 language model. The research initially employed a fine-tuning approach through Parameter-Efficient Fine-Tuning (PEFT), which, while effective in automating certain aspects such as ensuring consistency with formatting and style guidelines, required significant resources and time. To address these limitations, a prompt engineering approach was later adopted, significantly reducing the time required while improving the accuracy and consistency of reviews. By embedding a summary of CMS internal publication guidelines directly within the prompts, the model was able to perform without additional task-specific training. Both approaches demonstrated the potential for AI to streamline the peer review process, with the prompt engineering method showing particular promise in efficiency. The findings lay a strong foundation for future improvements, including further customization of training processes, dataset expansion, and the development of a graphical user interface (GUI), while also contributing valuable insights into the current capabilities and limitations of AI models in specialized domains.</p> |
| title | A CMS AI to Improve Paper Publishing |
| topic | CERN openlab summer student project |
| url | https://doi.org/10.5281/zenodo.13856034 |