Year: 2024
Type: conference-paper
Abstract: Large language models (LLMs) with Chainof-thought (CoT) have recently emerged as a powerful technique for eliciting reasoning to improve various downstream tasks.As most research mainly focuses on English, with few explorations in a multilingual context, the question of how reliable this reasoning capability is in different languages is still open.To address it directly, we study multilingual reasoning consistency across multiple languages, using popular open-source LLMs.First, we compile the first large-scale multilingual math reasoning dataset, mCoT-MATH, covering eleven diverse languages.Then, we introduce multilingual CoT instruction tuning to boost reasoning capability across languages, thereby improving model consistency.While existing LLMs show substantial variation across the languages we consider, and especially low performance for lesser resourced languages, our 7B parameter model mCoT achieves impressive consistency across languages, and superior or comparable performance to close-and open-source models even of much larger sizes.
Authors: Huiyuan Lai, Malvina Nissim
Institution: University of Groningen
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Rijksuniversiteit Groningen
OpenAlex ID: W4402672000