aiqbench analysis
This paper introduces MORFES, a novel benchmark addressing the critical gap in evaluating inflectional competence for Modern Greek within large language models. By focusing on lower-frequency lemmas rather than factual recall, it provides an essential metric for assessing grammatical rule adherence across open-weight architectures like LLaMA and Qwen3. This work is vital for researchers aiming to improve morphological handling in multilingual AI systems beyond simple knowledge retrieval.
MORFES: A Benchmark for Productive Inflectional Competence in Modern Greek
arXiv:2607.28274v1 Announce Type: new Abstract: Modern Greek is a richly inflected language, yet the language models built for it are evaluated mainly on factual knowledge, and no benchmark is dedicated to their inflectional competence. We introduce MORFES (Morphological Open-class Recognition-and-Formation Evaluation Suite), a benchmark of 500 expert-verified items that tests the recognition and production of Greek inflected forms, favoring lower-frequency lemmas so that a correct answer reflects the rule rather than a memorized form. We make it publicly available at https://huggingface.co/datasets/KIEFERSA/MORFES. We evaluate a range of open language models on MORFES, situating them within the rapidly scaling open-weight ecosystem from LLaMA to Qwen3, DeepSeek-R1, Magistral, and Kimi K2, where multilingual coverage grows but grammatical competence in morphologically rich languages remains under-measured. Among them, Sophea-Genesis-1, a model we developed and release as open weights at https://huggingface.co/KIEFERSA/Sophea-Genesis-1, leads on inflectional morphology while matching similarly sized models in general capability.
Abstract:Modern Greek is a richly inflected language, yet the language models built for it are evaluated mainly on factual knowledge, and no benchmark is dedicated to their inflectional competence. We introduce MORFES (Morphological Open-class Recognition-and-Formation Evaluation Suite), a benchmark of 500 expert-verified items that tests the recognition and production of Greek inflected forms, favoring lower-frequency lemmas so that a correct answer reflects the rule rather than a memorized form. We make it publicly available at this https URL. We evaluate a range of open language models on MORFES, situating them within the rapidly scaling open-weight ecosystem from LLaMA to Qwen3, DeepSeek-R1, Magistral, and Kimi K2, where multilingual coverage grows but grammatical competence in morphologically rich languages remains under-measured. Among them, Sophea-Genesis-1, a model we developed and release as open weights at this https URL, leads on inflectional morphology while matching similarly sized models in general capability.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2607.28274 [cs.CL]
(or arXiv:2607.28274v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.28274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 244 words
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