Papers with LMentry
LMentry: A Language Model Benchmark of Elementary Language Tasks (2023.findings-acl)
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| Challenge: | Large language models are evaluated via perplexity or performance on downstream tasks, but these benchmarks are too complex and difficult to inspect. |
| Approach: | They propose a benchmark that focuses on 25 tasks that humans are expected to perform perfectly, such as writing a sentence containing a specific word or identifying which words in a list belong to a certain category. |
| Outcome: | The proposed benchmarks show that large language models are performing better than previous benchmarks. |
Multi-LMentry: Can Multilingual LLMs Solve Elementary Tasks Across Languages? (2025.emnlp-main)
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Luca Moroni, Javier Aula-Blasco, Simone Conia, Irene Baucells, Naiara Perez, Silvia Paniagua Suárez, Anna Sallés, Malte Ostendorff, Júlia Falcão, Guijin Son, Aitor Gonzalez-Agirre, Roberto Navigli, Marta Villegas
| Challenge: | a recent study focused on complex, high-level tasks, but LMentry is limited to English . a multilingual evaluation of large language models is needed to address this gap, authors say . |
| Approach: | They propose a compact benchmark that enables systematic evaluation of large language models . they propose to use tasks that are trivial for humans but remain surprisingly difficult for LLMs . |
| Outcome: | The proposed benchmark is limited to English, leaving its insights linguistically narrow. |