1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?

• URL: http://arxiv.org/abs/2406.14721v1
• Date: Thu, 20 Jun 2024 20:32:53 GMT
• Title: 1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
• Authors: Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun,
• Abstract summary: Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they exhibit inconsistencies in handling identical queries in different languages, presenting challenges for further advancement. This paper introduces a method to enhance the multilingual performance of LLMs by aggregating knowledge from diverse languages.
• Score: 46.43162333819418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they exhibit inconsistencies in handling identical queries in different languages, presenting challenges for further advancement. This paper introduces a method to enhance the multilingual performance of LLMs by aggregating knowledge from diverse languages. This approach incorporates a low-resource knowledge detector specific to a language, a language selection process, and mechanisms for answer replacement and integration. Our experiments demonstrate notable performance improvements, particularly in reducing language performance disparity. An ablation study confirms that each component of our method significantly contributes to these enhancements. This research highlights the inherent potential of LLMs to harmonize multilingual capabilities and offers valuable insights for further exploration.

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