Summary of Disce Aut Deficere: Evaluating Llms Proficiency on the Invalsi Italian Benchmark, by Fabio Mercorio et al.
Disce aut Deficere: Evaluating LLMs Proficiency on the INVALSI Italian Benchmark
by Fabio Mercorio, Mario Mezzanzanica, Daniele Potertì, Antonio Serino, Andrea Seveso
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a structured benchmark for evaluating Large Language Models (LLMs) in languages other than English. The authors adapt the INVALSI tests, a well-established set of assessments measuring educational competencies, to automate LLM evaluation. This study makes three primary contributions: adapting the INVALSI benchmark for automated LLM assessment, providing an assessment of current LLMs, and visually comparing their performance against human results. The paper also invites researchers to submit their models for ongoing evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how computers can understand and create human language better. It’s trying to make sure these computer programs can work in different languages and cultures around the world. To do this, the authors are using a set of tests called INVALSI that measure what people know. They’re making these tests work for computers so we can see how well they’re doing compared to humans. |
