Chinese-Russian bilingual LLM for the China-Russia pipeline and its application in disaster scenarios

The China-Russia Pipeline is a significant economic and geopolitical infrastructure project that is essential to both nations' energy security and the advancement of bilateral relations. The development of a new Chinese-Russian bilingual LLM that focuses on catastrophe prevention, response, and recovery on this crucial pipeline is described in this research. To do this, we build a top-notch Chinese-Russian bilingual knowledge base to improve the LLM. To improve the model's reasoning and decision-making abilities, we also employ knowledge graphs and idea chain reasoning approaches. Given these considerations, the goal of this work is to provide a practical MMP of an LLM that is backed by empirical evaluations demonstrating that our approach outperforms general-purpose LLMs in terms of decision-making capabilities and even surpasses them in answering pipeline-related domain-specific queries.

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Publication Details

Published
2025-02-21
DOI
https://doi.org/10.1145/3728199.3728273
Primary Topic
Natural Language Processing Techniques
Type
article
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Chinese-Russian bilingual LLM for the China-Russia pipeline and its application in disaster scenarios

Chunyu Lu, Xin Hui, R. S. Shi, Jun Luo et al.
Natural Language Processing Techniques
article

Chinese-Russian bilingual LLM for the China-Russia pipeline and its application in disaster scenarios

Chunyu Lu, Xin Hui, R. S. Shi, Jun Luo, D Shang, Huihong He, Tianran Chen, Haoran Li
article en

Abstract

The China-Russia Pipeline is a significant economic and geopolitical infrastructure project that is essential to both nations' energy security and the advancement of bilateral relations. The development of a new Chinese-Russian bilingual LLM that focuses on catastrophe prevention, response, and recovery on this crucial pipeline is described in this research. To do this, we build a top-notch Chinese-Russian bilingual knowledge base to improve the LLM. To improve the model's reasoning and decision-making abilities, we also employ knowledge graphs and idea chain reasoning approaches. Given these considerations, the goal of this work is to provide a practical MMP of an LLM that is backed by empirical evaluations demonstrating that our approach outperforms general-purpose LLMs in terms of decision-making capabilities and even surpasses them in answering pipeline-related domain-specific queries.

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Natural Language Processing Techniques
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