← 研踪 PaperTrailarXiv:2611.10802 · v1
Evaluating Jev on Arabic: Comparing Decision Models and LLMs Across Arabic Tasks and Dialects
首次提交 2026-11-07 23:30
版本更新 2026-11-07 23:30
雷达观测 2026-11-10 11:14
摘要
How does Jev compare with other systems on Arabic tasks, and how does its performance change between Modern Standard Arabic (MSA) and dialects? We evaluate 67 systems, including 47 decision models and 20 large language models (LLMs), on 43 tasks across 9 task families using a shared zero-shot protocol. The Arabic-focused frozen suite includes English controls in three parallel resources, which we also remove in a separate descriptive comparison. Jev 1.14.0 achieves a mean task-primary metric of 69.24 on a scale multiplied by 100, compared with 68.77 for Jev 1.13.0 and 71.81 for Gemma-4-31B, the highest-scoring system on this aggregate. Removing the recorded English controls, repeated cross-task items or both leaves the full system order unchanged. Relative performance and gains between Jev versions vary across task families. In content-matched comparisons within Jev 1.14.0, pooled dialect-minus-MSA accuracy gaps range from -7.94 to -0.35 percentage points across four datasets; dialect-specific results show larger variation, and SIB-200's source-clustered interval includes zero. Paired contrasts with Jev 1.13.0, Gemma-4-31B and Jais-2-8B show a larger Palestinian banking penalty for Jev 1.14.0 than for either LLM under simultaneous uncertainty; the other gap contrasts remain inconclusive. These descriptive findings show why aggregate scores should be read alongside task-family results, coverage and matched language comparisons. They do not establish a general model ranking for dialect robustness or explain the causes of the observed gaps.
作者备注：28 pages, including references and appendices
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-12 08:13 · 最新观测版本
Evaluating Jev on Arabic: Comparing Decision Models and LLMs Across Arabic Tasks and Dialects
How does Jev compare with other systems on Arabic tasks, and how does its performance change between Modern Standard Arabic (MSA) and dialects? We evaluate 67 systems, including 47 decision models and 20 large language models (LLMs), on 43 tasks across 9 task families using a shared zero-shot protocol. The Arabic-focused frozen suite includes English controls in three parallel resources, which we also remove in a separate descriptive comparison. Jev 1.14.0 achieves a mean task-primary metric of 69.24 on a scale multiplied by 100, compared with 68.77 for Jev 1.13.0 and 71.81 for Gemma-4-31B, the highest-scoring system on this aggregate. Removing the recorded English controls, repeated cross-task items or both leaves the full system order unchanged. Relative performance and gains between Jev versions vary across task families. In content-matched comparisons within Jev 1.14.0, pooled dialect-minus-MSA accuracy gaps range from -7.94 to -0.35 percentage points across four datasets; dialect-specific results show larger variation, and SIB-200's source-clustered interval includes zero. Paired contrasts with Jev 1.13.0, Gemma-4-31B and Jais-2-8B show a larger Palestinian banking penalty for Jev 1.14.0 than for either LLM under simultaneous uncertainty; the other gap contrasts remain inconclusive. These descriptive findings show why aggregate scores should be read alongside task-family results, coverage and matched language comparisons. They do not establish a general model ranking for dialect robustness or explain the causes of the observed gaps.
查看此版本解读 →查看此版本原文 ↗v1 · 2026-11-07 23:30
Evaluating Jev on Arabic: Comparing Decision Models and LLMs Across Arabic Tasks and Dialects
How does Jev compare with other systems on Arabic tasks, and how does its performance change between Modern Standard Arabic (MSA) and dialects? We evaluate 67 systems, including 47 decision models and 20 large language models (LLMs), on 43 tasks across 9 task families using a shared zero-shot protocol. The Arabic-focused frozen suite includes English controls in three parallel resources, which we also remove in a separate descriptive comparison. Jev 1.14.0 achieves a mean task-primary metric of 69.24 on a scale multiplied by 100, compared with 68.77 for Jev 1.13.0 and 71.81 for Gemma-4-31B, the highest-scoring system on this aggregate. Removing the recorded English controls, repeated cross-task items or both leaves the full system order unchanged. Relative performance and gains between Jev versions vary across task families. In content-matched comparisons within Jev 1.14.0, pooled dialect-minus-MSA accuracy gaps range from -7.94 to -0.35 percentage points across four datasets; dialect-specific results show larger variation, and SIB-200's source-clustered interval includes zero. Paired contrasts with Jev 1.13.0, Gemma-4-31B and Jais-2-8B show a larger Palestinian banking penalty for Jev 1.14.0 than for either LLM under simultaneous uncertainty; the other gap contrasts remain inconclusive. These descriptive findings show why aggregate scores should be read alongside task-family results, coverage and matched language comparisons. They do not establish a general model ranking for dialect robustness or explain the causes of the observed gaps.
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