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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2406.17535 |
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| _version_ | 1866913404666511360 |
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| author | Mercorio, Fabio Mezzanzanica, Mario Potertì, Daniele Serino, Antonio Seveso, Andrea |
| author_facet | Mercorio, Fabio Mezzanzanica, Mario Potertì, Daniele Serino, Antonio Seveso, Andrea |
| contents | Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to generate and manipulate human language, highlighting their potential across various applications. Evaluating LLMs in languages other than English is crucial for ensuring their linguistic versatility, cultural relevance, and applicability in diverse global contexts, thus broadening their usability and effectiveness. We tackle this challenge by introducing a structured benchmark using the INVALSI tests, a set of well-established assessments designed to measure educational competencies across Italy. Our study makes three primary contributions: Firstly, we adapt the INVALSI benchmark for automated LLM evaluation, which involves rigorous adaptation of the test format to suit automated processing while retaining the essence of the original tests. Secondly, we provide a detailed assessment of current LLMs, offering a crucial reference point for the academic community. Finally, we visually compare the performance of these models against human results. Additionally, researchers are invited to submit their models for ongoing evaluation, ensuring the benchmark remains a current and valuable resource. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17535 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Disce aut Deficere: Evaluating LLMs Proficiency on the INVALSI Italian Benchmark Mercorio, Fabio Mezzanzanica, Mario Potertì, Daniele Serino, Antonio Seveso, Andrea Computation and Language Artificial Intelligence Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to generate and manipulate human language, highlighting their potential across various applications. Evaluating LLMs in languages other than English is crucial for ensuring their linguistic versatility, cultural relevance, and applicability in diverse global contexts, thus broadening their usability and effectiveness. We tackle this challenge by introducing a structured benchmark using the INVALSI tests, a set of well-established assessments designed to measure educational competencies across Italy. Our study makes three primary contributions: Firstly, we adapt the INVALSI benchmark for automated LLM evaluation, which involves rigorous adaptation of the test format to suit automated processing while retaining the essence of the original tests. Secondly, we provide a detailed assessment of current LLMs, offering a crucial reference point for the academic community. Finally, we visually compare the performance of these models against human results. Additionally, researchers are invited to submit their models for ongoing evaluation, ensuring the benchmark remains a current and valuable resource. |
| title | Disce aut Deficere: Evaluating LLMs Proficiency on the INVALSI Italian Benchmark |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.17535 |