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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.16182 |
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| _version_ | 1866916109279559680 |
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| author | Gesnouin, Joseph Tannier, Yannis Da Silva, Christophe Gomes Tapory, Hatim Brier, Camille Simon, Hugo Rozenberg, Raphael Woehrel, Hermann Yakaabi, Mehdi El Binder, Thomas Marie, Guillaume Caron, Emilie Nogueira, Mathile Fontas, Thomas Puydebois, Laure Theophile, Marie Morandi, Stephane Petit, Mael Creissac, David Ennouchy, Pauline Valetoux, Elise Visade, Celine Balloux, Severine Cortes, Emmanuel Devineau, Pierre-Etienne Tan, Ulrich Mac Namara, Esther Yang, Su |
| author_facet | Gesnouin, Joseph Tannier, Yannis Da Silva, Christophe Gomes Tapory, Hatim Brier, Camille Simon, Hugo Rozenberg, Raphael Woehrel, Hermann Yakaabi, Mehdi El Binder, Thomas Marie, Guillaume Caron, Emilie Nogueira, Mathile Fontas, Thomas Puydebois, Laure Theophile, Marie Morandi, Stephane Petit, Mael Creissac, David Ennouchy, Pauline Valetoux, Elise Visade, Celine Balloux, Severine Cortes, Emmanuel Devineau, Pierre-Etienne Tan, Ulrich Mac Namara, Esther Yang, Su |
| contents | This report introduces LLaMandement, a state-of-the-art Large Language Model, fine-tuned by the French government and designed to enhance the efficiency and efficacy of processing parliamentary sessions (including the production of bench memoranda and documents required for interministerial meetings) by generating neutral summaries of legislative proposals. Addressing the administrative challenges of manually processing a growing volume of legislative amendments, LLaMandement stands as a significant legal technological milestone, providing a solution that exceeds the scalability of traditional human efforts while matching the robustness of a specialized legal drafter. We release all our fine-tuned models and training data to the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16182 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLaMandement: Large Language Models for Summarization of French Legislative Proposals Gesnouin, Joseph Tannier, Yannis Da Silva, Christophe Gomes Tapory, Hatim Brier, Camille Simon, Hugo Rozenberg, Raphael Woehrel, Hermann Yakaabi, Mehdi El Binder, Thomas Marie, Guillaume Caron, Emilie Nogueira, Mathile Fontas, Thomas Puydebois, Laure Theophile, Marie Morandi, Stephane Petit, Mael Creissac, David Ennouchy, Pauline Valetoux, Elise Visade, Celine Balloux, Severine Cortes, Emmanuel Devineau, Pierre-Etienne Tan, Ulrich Mac Namara, Esther Yang, Su Computation and Language Artificial Intelligence This report introduces LLaMandement, a state-of-the-art Large Language Model, fine-tuned by the French government and designed to enhance the efficiency and efficacy of processing parliamentary sessions (including the production of bench memoranda and documents required for interministerial meetings) by generating neutral summaries of legislative proposals. Addressing the administrative challenges of manually processing a growing volume of legislative amendments, LLaMandement stands as a significant legal technological milestone, providing a solution that exceeds the scalability of traditional human efforts while matching the robustness of a specialized legal drafter. We release all our fine-tuned models and training data to the community. |
| title | LLaMandement: Large Language Models for Summarization of French Legislative Proposals |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16182 |