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Multilingualism as a Key to Improved Logical Reasoning in Large Language Models?

Large language models (LLMs) have revolutionized the way we interact with information. They write texts, translate languages, and answer complex questions in seconds. Despite their impressive capabilities, studies show that these models often exhibit an "English-language bias." This means they frequently perform better on tasks presented in English than in other languages. However, new research suggests that multilingualism could significantly improve the logical reasoning abilities of LLMs.

A recent study investigates the potential of multilingual thinking in LLMs and finds that using different languages for reasoning tasks can lead to significantly better results than using English exclusively. The researchers were able to demonstrate that multilingual thinking can increase performance in certain scenarios by up to 10 percentage points (Acc@k). This performance gain is not only significant but also robust against variations in translation quality and language selection.

The reasons for this positive effect are manifold. Firstly, processing information in multiple languages expands the model's knowledge horizon. Different languages encode information and cultural contexts differently, contributing to a more comprehensive understanding of the world. Secondly, translation between languages can be considered a kind of "thinking process" that forces the model to grasp the underlying concepts and connections more deeply.

However, the study also highlights the challenges associated with implementing multilingual thinking. Current answer selection methods are often unable to fully exploit the potential of multilingual information. Their inherent limitations and biases lead to important information being lost or misinterpreted. This underscores the need for further research to develop more effective strategies for integrating multilingualism into LLMs.

The results of this study are particularly relevant for companies like Mindverse, which specialize in the development of AI-powered content solutions. Integrating multilingual thinking into tools such as chatbots, voicebots, AI search engines, and knowledge systems could significantly increase their performance and efficiency. This opens up new possibilities for developing innovative applications that meet the needs of a globalized world.

Research in the field of multilingual thinking in LLMs is still in its early stages. However, the results so far suggest that multilingualism could be a key to unlocking the full potential of these models. The development of new methods for integrating and utilizing multilingual information will be crucial for creating the next generation of AI systems that are more intelligent, robust, and versatile.

Bibliographie: https://arxiv.org/abs/2504.11833 https://arxiv.org/html/2504.11833v1 http://paperreading.club/page?id=300081 https://twitter.com/HEI/status/1913034486515716376 https://openreview.net/forum?id=S6cBH99BhB https://nips.cc/virtual/2024/poster/95346 https://twitter.com/i/status/1912901753692958888 https://www.researchgate.net/publication/376403328_Not_All_Languages_Are_Created_Equal_in_LLMs_Improving_Multilingual_Capability_by_Cross-Lingual_Thought_Prompting https://huggingface.co/papers/2502.06772 https://openreview.net/pdf/b306ebc7e3c5a15ccb3741fc7db5660bd3375d4a.pdf ```