Yuxia Wang, Rui Xing, Jonibek Mansurov, Giovanni Puccetti, Zhuohan Xie, Minh Ngoc Ta, Jiahui Geng, Jinyan Su, Mervat Abassy, Saadeldine Eletter, Kareem Elozeiri, Nurkhan Laiyk, Maiya Goloburda, Tarek Mahmoud, Raj Vardhan Tomar, Alexander Aziz, Ryuto Koike, Masahiro Kaneko, Artem Shelmanov, Ekaterina Artemova, Vladislav Mikhailov, Akim Tsvigun, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Abstract We introduce GSM-Identity, a pipeline to modify existing mathematical reasoning benchmarks by adding extra complexity to the questions while preserving their fundamental meaning. By systematically transforming numerical values in the GSM8K dataset into mathematically equivalent but less obvious expressions, we create a benchmark to measure Large Language Models (LLMs) mathematical understanding. We evaluate LLMs ranging from 7 billions to 72 billions parameters using multiple prompting strategies, including standard, notice-based, and chain-of-thought approaches. We find that Math oriented models can retain most of their performance on GSM8K when evaluated on GSM-Identity, while general purpose models show significant performance degradation. A comparison with human evaluations reveals that models in the 7 billion parameters range perform similar to humans when exposed to the kind of modifications we study, while models with more than 70 billion parameters are more accurate than humans in answering the questions and they are also more resilient to modifications. Our findings highlight GSM-Identity as a valuable tool for distinguishing reasoning from memorization, offering insights into the abilities of LLMs to understand higher level mathematical concepts.

While Italian is a high-resource language, there are few Italian-native benchmarks to evaluate generative Large Language Models (LLMs) in this language. This work presents three new benchmarks: Invalsi MATE to evaluate models performance on mathematical understanding in Italian, Invalsi ITA to evaluate language under standing in Italian and Olimpiadi MATE for more complex mathematical understanding. The first two benchmarks are based on the Invalsi tests, which are administered to students of age between 6 and 18 within the Italian school system and have been validated by several experts in teaching and pedagogy, the third one comes from the Italian highschool math Olympics. We evaluate 10 powerful language models on these benchmarks and we find that they are bound by 71% accuracy on Invalsi MATE, achieved by Llama 3.1 70b instruct and by 88% on Invalsi ITA. For both Invalsi MATE and Invalsi ITA we compare LLMs with the average performance of Italian students to show that Llama 3.1 is the only one to outperform them on Invalsi MATE while most models do so on Invalsi ITA, we then show that Olimpiadi MATE is more challenging than Invalsi MATE and the highest accuracy, achieved by Llama 3.1 405b instruct accuracy is 45%.

In studies of transferable learning, scaling laws are obtained for various important foundation models to predict their properties and performance at larger scales. Taking language-vision learning as example, we show here how scaling law derivation can also be used for model and dataset comparison, allowing to decide which procedure is to be preferred for pre-training. Full scaling laws based on dense measurements across a wide span of model and samples seen scales are derived for two important language-vision learning procedures, CLIP and MaMMUT, that use either contrastive only or contrastive and captioning text generative loss. For the first time, we use derived scaling laws to compare both models and three open datasets, DataComp-1.4B, Re-LAION-1.4B and DFN-1.4B, while ensuring sufficient prediction accuracy on held out points. From comparison, we obtain evidence for (i) MaMMUT's stronger improvement with scale and better sample efficiency than standard CLIP (ii) DFN-1.4B outperforming other open datasets. To strengthen validity of the comparison, we show scaling laws for various downstream tasks, classification, retrieval, and segmentation, observing consistently the same scaling trends for models and datasets across tasks. We show that comparison can also be performed when deriving scaling laws with a constant learning rate schedule, reducing compute cost. Accurate derivation of scaling laws provides thus means to perform model and dataset comparison on aligned common compute axis across large scale span, avoiding misleading conclusions based on measurements from few isolated single reference scales only. This paves road for guided collective improvement of open foundation models and training datasets, as scaling law based comparisons from various studies executed in common frame can be combined to identify overall better procedures. We release all the pre-trained models with their intermediate checkpoints, including openMaMMUT-L/14, which achieves 80.3% zero-shot ImageNet-1k accuracy, trained on 12.8B samples from DataComp-1.4B. Code for reproducing experiments in the paper and raw experiments data can be found at https://github.com/LAION-AI/scaling-laws-for-comparison.

Giovanni Puccetti, Anna Rogers, Chiara Alzetta, Felice Dell’Orletta, Andrea Esuli. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.