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arxiv: 2605.07731 · v1 · submitted 2026-05-08 · 💻 cs.CL · cs.AI

Recognition: no theorem link

Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs

Andrea Sassella , Andrea Chizzola , Tommaso Bianchi , Luca Alessandrelli , Mark James Carman
Authors on Pith no claims yet

Pith reviewed 2026-05-11 03:35 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords LLM benchmarkingItalian language modelsMixture of Expertsopen-source LLMsperformance evaluationnative language AI
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The pith

EngGPT2MoE-16B-A3B matches or exceeds other Italian open-source LLMs on most benchmarks while trailing some top international models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper evaluates the EngGPT2MoE-16B-A3B, a 16-billion-parameter Mixture-of-Experts model with 3 billion active parameters, against comparable Italian models like Velvet-14B and LLaMAntino-3-ANITA-8B as well as international ones such as DeepSeek-MoE-16B and Llama-3.1-8B. Testing spans international tasks including math reasoning on GSM8K and AIME, coding on HumanEval, knowledge on MMLU, and long-context handling on RULER, plus the Italian-specific ITALIC dataset. The model delivers equal or higher scores than most Italian comparators across these tests and outperforms DeepSeek-MoE on every benchmark considered. When all metrics are aggregated the new model ranks above the other Italian models evaluated, establishing it as an improvement for native-language systems.

Core claim

EngGPT2MoE-16B-A3B performs as well or better than the compared Italian models on international benchmarks and the longest-context RULER setting, exceeds DeepSeek-MoE-16B-Chat on all tests, and when scores are combined across every benchmark shows higher overall performance than other Italian models under evaluation while remaining below certain leading international models.

What carries the argument

Side-by-side performance comparison of the 16B MoE model against other MoE and dense models on a fixed suite of benchmarks that includes ARC-Challenge, GSM8K, AIME24/25, MMLU, HumanEval, ITALIC, BFCL, and RULER at multiple context lengths.

If this is right

  • Native Italian developers gain a stronger open starting point than prior models for further fine-tuning or deployment.
  • Mixture-of-Experts scaling at the 16B total / 3B active size can deliver competitive results on both general and language-specific tasks.
  • Continued investment in Italian training data and evaluation sets is likely to produce further gains in local model quality.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The remaining gap to the strongest international models suggests that larger Italian-specific datasets or longer training runs could close performance differences.
  • Repeating the same benchmark protocol on other low-resource languages could test whether the observed pattern of steady local improvement holds more broadly.
  • Industry users needing Italian-language capabilities may now prefer this model over earlier open alternatives for production tasks.

Load-bearing premise

The chosen benchmarks and the set of comparison models give a fair, unbiased, and sufficiently complete view of the model's capabilities, especially on Italian-specific tasks and under identical evaluation conditions.

What would settle it

A standardized Italian-language benchmark focused on cultural or idiomatic content where EngGPT2MoE-16B-A3B scores below all the Italian models it currently leads would undermine the claim that it constitutes a step forward.

Figures

Figures reproduced from arXiv: 2605.07731 by Andrea Chizzola, Andrea Sassella, Luca Alessandrelli, Mark James Carman, Tommaso Bianchi.

Figure 1
Figure 1. Figure 1: Comparable open-source models in April 2026. The horizontal axis shows the total parameter [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of popular potential datasets, including their popularity (measured by yearly down [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison with Italian models across the considered benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison with Mixture-of-Experts models across the considered benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison with the larger dense models across the considered benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison with the smaller dense models across the considered benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Average ranking of models across the various benchmark datasets. The ranking for each [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Average of the accuracy score of models across the various benchmarks datasets. Each bench [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Bonferroni-adjusted pairwise significance heatmaps for ARC-Challenge under three settings: [PITH_FULL_IMAGE:figures/full_fig_p030_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: GSM8K, llama | strict_match. Bonferroni-adjusted pairwise significance heatmap for GSM8K under the strict_match metric. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Bonferroni-adjusted pairwise significance heatmaps for AIME24 under the [PITH_FULL_IMAGE:figures/full_fig_p032_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Bonferroni-adjusted pairwise significance heatmaps for AIME25 under the [PITH_FULL_IMAGE:figures/full_fig_p033_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: . Bonferroni-adjusted pairwise significance heatmap for MMLU under the [PITH_FULL_IMAGE:figures/full_fig_p034_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Bonferroni-adjusted pairwise significance heatmaps for the MMLU-Redux subsets Humanities, [PITH_FULL_IMAGE:figures/full_fig_p035_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Bonferroni-adjusted pairwise significance heatmaps for HumanEval under the [PITH_FULL_IMAGE:figures/full_fig_p036_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Bonferroni-adjusted pairwise significance heatmap for the BFCL [PITH_FULL_IMAGE:figures/full_fig_p036_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Bonferroni-adjusted pairwise significance heatmaps for ITALIC under the settings [PITH_FULL_IMAGE:figures/full_fig_p037_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Bonferroni-adjusted pairwise significance heatmaps for RULER at context lengths 4096, [PITH_FULL_IMAGE:figures/full_fig_p038_18.png] view at source ↗
read the original abstract

This report benchmarks the performance of ENGINEERING Ingegneria Informatica S.p.A.'s EngGPT2MoE-16B-A3B LLM, a 16B parameter Mixture of Experts (MoE) model with 3B active parameters. Performance is investigated across a wide variety of representative benchmarks, and is compared against comparably-sized open-source MoE and dense models. In comparison with popular Italian models, namely FastwebMIIA-7B, Minerva-7B, Velvet-14B, and LLaMAntino-3-ANITA-8B, EngGPT2MoE-16B-A3B performs as well or better on international benchmarks: ARC-Challenge, GSM8K, AIME24, AIME25, MMLU, and HumanEval (HE). It achieves the best performance for the longest context setting (32k) of the RULER benchmark. On the Italian benchmark dataset ITALIC, the model performs as well or better than the other models except for Velvet-14B, which outperforms it. Compared with popular MoE models of comparable size, the new model reports higher values than DeepSeek-MoE-16B-Chat on all considered benchmarks. It has higher values than Moonlight-16B-A3B on HE, MMLU, AIME24, AIME25, GSM8K, and the 32k RULER setting, but lower on BFCL and some ARC and ITALIC settings. Finally it has lower values than GPT-OSS-20B on most benchmarks, including HE, MMLU, AIME24, AIME25, GSM8K, ARC, BFCL, and the RULER 32k. When compared with popular dense models, EngGPT2MoE-16B-A3B reports higher values on AIME24 and AIME25 than Llama-3.1-8B-Instruct, Gemma-3-12b-it, and Ministral-3-8BInstruct-2512-BF16, but lower values on ITALIC, BFCL, and RULER with a 32k context. When performance is aggregated across all benchmark metrics, EngGPT2MoE-16B-A3B shows higher performance than the Italian models under evaluation while achieving lower results than some of the most performant international models, in particular GPT-5 nano and Qwen3-8B. Taken together, our findings find the new model to be a step forward for native Italian Large Language Models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript benchmarks EngGPT2MoE-16B-A3B, a 16B-parameter Mixture-of-Experts model with 3B active parameters, against Italian models (FastwebMIIA-7B, Minerva-7B, Velvet-14B, LLaMAntino-3-ANITA-8B) and international open-source models (DeepSeek-MoE-16B-Chat, Moonlight-16B-A3B, GPT-OSS-20B, Llama-3.1-8B-Instruct, Gemma-3-12b-it, Ministral-3-8B-Instruct) on ARC-Challenge, GSM8K, AIME24, AIME25, MMLU, HumanEval, BFCL, RULER (including 32k context), and the Italian ITALIC benchmark. It reports that the new model matches or exceeds the Italian comparators on most international tasks, leads on RULER 32k, trails Velvet-14B on ITALIC, outperforms DeepSeek-MoE on all tasks, is mixed versus Moonlight, trails GPT-OSS-20B on most metrics, and shows mixed results versus dense models, concluding that the model constitutes a step forward for native Italian LLMs.

Significance. If the reported scores reflect standardized evaluation conditions, the work supplies concrete comparative data on a new Italian MoE architecture, documenting relative strengths in mathematical reasoning and long-context handling that could guide subsequent Italian-language model development.

major comments (3)
  1. [Evaluation section] Evaluation section: The manuscript provides no information on whether benchmark scores for the comparison models were obtained by re-evaluating them inside the same inference harness, using identical prompt templates, few-shot counts, decoding parameters (temperature, top-p), and post-processing as applied to EngGPT2MoE-16B-A3B. Because the abstract and results sections rest the central claim of being “a step forward” entirely on these relative numbers, the absence of this protocol detail is load-bearing.
  2. [Results section] Results section and associated tables: No error bars, standard deviations, or statistical tests are reported for any per-benchmark or aggregated scores. Claims such as “performs as well or better,” “higher values,” and “higher performance than the Italian models” therefore cannot be assessed for reliability when differences may be within normal run-to-run variation.
  3. [Results section] Results section: The aggregation procedure used to conclude that EngGPT2MoE-16B-A3B shows “higher performance than the Italian models under evaluation” is not described; it is unclear which subset of metrics is included, how they are normalized or weighted, and whether every model was scored on exactly the same task instances.
minor comments (2)
  1. [Abstract] Abstract: The title uses “EngGPT2-16B-A3B” while the body consistently uses “EngGPT2MoE-16B-A3B”; a single consistent name would reduce reader confusion.
  2. [Results section] Results section: The paper would benefit from an explicit limitations paragraph addressing the scope of Italian-specific evaluation (only ITALIC is used) and the reliance on public benchmark scores for some comparators.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough and constructive review. The comments identify important gaps in methodological transparency that we will address in the revision. Below we respond point by point to the major comments.

read point-by-point responses

  1. Referee: [Evaluation section] Evaluation section: The manuscript provides no information on whether benchmark scores for the comparison models were obtained by re-evaluating them inside the same inference harness, using identical prompt templates, few-shot counts, decoding parameters (temperature, top-p), and post-processing as applied to EngGPT2MoE-16B-A3B. Because the abstract and results sections rest the central claim of being “a step forward” entirely on these relative numbers, the absence of this protocol detail is load-bearing.

    Authors: We agree that explicit documentation of the shared evaluation protocol is necessary to support the comparative claims. All models were evaluated under identical conditions using the EleutherAI lm-evaluation-harness (v0.4.2) with the same prompt templates, few-shot counts, decoding parameters (temperature=0.0 for deterministic generation on most tasks, top-p=1.0), and post-processing scripts. We will add a dedicated paragraph in the Evaluation section describing the full protocol, the exact version of the harness, and confirmation that every comparator was run through this pipeline rather than relying solely on published numbers. revision: yes

  2. Referee: [Results section] Results section and associated tables: No error bars, standard deviations, or statistical tests are reported for any per-benchmark or aggregated scores. Claims such as “performs as well or better,” “higher values,” and “higher performance than the Italian models” therefore cannot be assessed for reliability when differences may be within normal run-to-run variation.

    Authors: We acknowledge that the absence of variability measures limits the strength of the “as well or better” claims. All reported numbers reflect single runs because of the prohibitive cost of repeated inference across 16B-scale models on the full benchmark suite. In the revision we will add an explicit limitations paragraph stating this constraint, note that performance advantages appear consistently across nine distinct benchmarks, and include any available multi-seed results for the smaller tasks (e.g., GSM8K) where they were obtained. We will also qualify the language in the abstract and results to reflect the single-run nature of the data. revision: partial

  3. Referee: [Results section] Results section: The aggregation procedure used to conclude that EngGPT2MoE-16B-A3B shows “higher performance than the Italian models under evaluation” is not described; it is unclear which subset of metrics is included, how they are normalized or weighted, and whether every model was scored on exactly the same task instances.

    Authors: We apologize for the missing description. The aggregate comparison was obtained by (i) restricting to the nine tasks evaluated for every model (ARC-Challenge, GSM8K, AIME24, AIME25, MMLU, HumanEval, BFCL, RULER-32k, ITALIC), (ii) min-max normalizing each metric across the Italian models only, and (iii) averaging the normalized scores. All models were scored on identical task instances and splits. We will insert a precise description of this procedure, list the exact metrics, and add an appendix table showing the per-metric normalized values and the final aggregate for transparency. revision: yes

Circularity Check

0 steps flagged

No circularity: pure empirical benchmarking with external scores

full rationale

The paper contains no derivations, equations, fitted parameters, or self-referential logic. Its claims consist entirely of direct comparisons of benchmark scores (ARC-Challenge, GSM8K, MMLU, ITALIC, RULER, etc.) against other models. These scores are treated as external inputs; the paper performs no internal fitting, prediction, or re-derivation that could reduce a result to its own inputs by construction. Self-citations, if present, are not load-bearing for any central claim. The argument is therefore self-contained as a report of observed performance differences.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a purely empirical benchmarking study. No free parameters are fitted, no axioms are invoked, and no new entities are postulated; all claims derive from reported benchmark scores.

pith-pipeline@v0.9.0 · 5803 in / 1094 out tokens · 68748 ms · 2026-05-11T03:35:11.922587+00:00 · methodology

discussion (0)

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