arXiv:2606.17905v1 Announce Type: new Abstract: Large language models perform increasingly well on standardized logical reasoning benchmarks, but whether this ability remains robust beyond English is unclear. We introduce ChLogic, an English--Chinese aligned benchmark that tests whether models preserve logical reasoning performance when the same latent logical structure is expressed in English and diverse Chinese surface realizations. Built from formal logical templates, the benchmark contains three data sets: (i) the General aligned set, derived from 60 General Propositions across nine templa
The proliferation of increasingly capable large language models necessitates rigorous cross-lingual robustness testing to understand their limitations and ensure equitable performance globally.
This benchmark reveals the crucial, under-evaluated challenge of maintaining logical reasoning robustness in LLMs across non-English languages, which is vital for global AI adoption and equitable development.
The explicit focus on evaluating logical reasoning in diverse Chinese expressions introduces a new, critical dimension to LLM assessment beyond English-centric benchmarks.
- · Chinese language AI developers
- · Multilingual AI research
- · AI fairness and ethics researchers
- · LLMs with poor cross-lingual generalization
- · English-centric AI evaluation methodologies
Increased research and development efforts will focus on improving logical reasoning in LLMs for non-English languages.
New techniques will emerge that specifically address cultural and linguistic nuances in logical expression to enhance AI performance.
This could accelerate the development of truly universal AI agents capable of robust reasoning across diverse linguistic and cultural contexts, reducing digital divides.
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Read at arXiv cs.CL