Language models struggle with multilingual reasoning, new paper finds

A new research paper explores the reasoning capabilities of language models across different languages. The study found that models struggle with multilingual reasoning, often failing to decompose complex questions into logical steps. This lack of faithful step-by-step inference leads to composition failures in answering two-hop questions. To address this, the researchers propose a SUBQ prompting method that guides multi-step reasoning with sub-questions, significantly improving accuracy. AI

IMPACT Highlights limitations in current language models for multilingual tasks and proposes a method to improve reasoning.

RANK_REASON Research paper published on arXiv detailing findings about language model reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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