analiza aiqbench
This study benchmarks large language models on complex Arabic Islamic inheritance cases requiring multi-step legal reasoning and precise numerical computation. Results indicate a significant reliability gap where commercial models, specifically Gemini 2.5 Flash, outperform open-source alternatives in maintaining consistency across dependent logical steps. The findings highlight critical challenges for deploying LLMs in high-stakes domains demanding strict adherence to structured rules without task-specific adaptation.
Which Models Perform Better in Inheritance Reasoning?
arXiv:2606.13751v2 Announce Type: replace Abstract: This paper presents the participation of team PSL in the QIAS 2026 Shared Task on Arabic Islamic inheritance reasoning. The task evaluates the ability of large language models to solve inheritance cases that require legal interpretation, multi-step reasoning, and precise numerical computation. We compare \textit{commercial} and \textit{open-source} models under a unified prompting strategy to assess their effectiveness in structured legal reasoning with minimal task-specific adaptation. \\ Our results show a clear gap in reliability between the two model families. Commercial models demonstrate stronger performance in identifying eligible heirs, applying exclusion rules, and maintaining consistency across reasoning steps. In contrast, open-source models exhibit greater instability, particularly in cases involving dependent legal decisions and fractional share adjustments. The best performance is achieved by \textit{Gemini 2.5 Flash}, with an MRE of $0.989$.
Title:Which Models Perform Better in Inheritance Reasoning?
Abstract:This paper presents the participation of team PSL in the QIAS 2026 Shared Task on Arabic Islamic inheritance reasoning. The task evaluates the ability of large language models to solve inheritance cases that require legal interpretation, multi-step reasoning, and precise numerical computation. We compare \textit{commercial} and \textit{open-source} models under a unified prompting strategy to assess their effectiveness in structured legal reasoning with minimal task-specific adaptation. \\ Our results show a clear gap in reliability between the two model families. Commercial models demonstrate stronger performance in identifying eligible heirs, applying exclusion rules, and maintaining consistency across reasoning steps. In contrast, open-source models exhibit greater instability, particularly in cases involving dependent legal decisions and fractional share adjustments. The best performance is achieved by \textit{Gemini 2.5 Flash}, with an MRE of $0.989$.
Computation and Language (cs.CL)
arXiv:2606.13751 [cs.CL]
(or arXiv:2606.13751v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.13751 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 229 cuvinte
Subiecte conexe

[AINews] not much happened today
Latent Space · 18 sept. 2026

[AINews] Claude Opus 5.5, the new default model for AINews — and everybody cuts prices 40-50%
Latent Space · 23 sept. 2026
Construct validity of Claude Opus 4.8's System Card – A commentary
LessWrong · 12 iun. 2026
Hyperloom: A Multi-Agent Harness for Autonomous Inference Optimization on AMD GPUs
AMD · 21 sept. 2026