New multilingual benchmark assesses LLM math solvability detection

A new study published on arXiv introduces the first multilingual benchmark for assessing Large Language Models' (LLMs) ability to detect mathematical solvability. The benchmark extends the existing ReliableMath dataset to include problems in French and Greek, alongside English. Researchers found that LLMs encode solvability belief in a largely universal, language-agnostic manner, but higher-resource languages like English show lower faithfulness in detecting solvability. AI

IMPACT This research could lead to more robust mathematical reasoning in multilingual LLMs by highlighting language-agnostic belief encoding and faithfulness issues.

RANK_REASON The cluster contains a research paper detailing a new benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• English
• French
• Greek
• Hugging Face
• Large Language Models
• Maria-Eleni Zoumpoulidi
• ReliableMath

• paper
• model release

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