Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain

• URL: http://arxiv.org/abs/2507.16974v1
• Date: Tue, 22 Jul 2025 19:25:10 GMT
• Title: Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
• Authors: Rishemjit Kaur, Arshdeep Singh Bhankhar, Surangika Ranathunga, Jashanpreet Singh Salh, Sudhir Rajput, Vidhi, Kashish Mahendra, Bhavika Berwal, Ritesh Kumar,
• Abstract summary: Large language models (LLMs) in agriculture typically offer generic advisories, lacking precision in local and multilingual contexts.<n>This study generates multilingual synthetic agricultural datasets (English, Hindi, Punjabi) from agriculture-specific documents and fine-tuning language-specific LLMs.<n>Our evaluation on curated multilingual datasets demonstrates significant improvements in factual accuracy, relevance, and agricultural consensus.
• Score: 1.0144032120138065
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Enabling farmers to access accurate agriculture-related information in their native languages in a timely manner is crucial for the success of the agriculture field. Although large language models (LLMs) can be used to implement Question Answering (QA) systems, simply using publicly available general-purpose LLMs in agriculture typically offer generic advisories, lacking precision in local and multilingual contexts due to insufficient domain-specific training and scarcity of high-quality, region-specific datasets. Our study addresses these limitations by generating multilingual synthetic agricultural datasets (English, Hindi, Punjabi) from agriculture-specific documents and fine-tuning language-specific LLMs. Our evaluation on curated multilingual datasets demonstrates significant improvements in factual accuracy, relevance, and agricultural consensus for the fine-tuned models compared to their baseline counterparts. These results highlight the efficacy of synthetic data-driven, language-specific fine-tuning as an effective strategy to improve the performance of LLMs in agriculture, especially in multilingual and low-resource settings. By enabling more accurate and localized agricultural advisory services, this study provides a meaningful step toward bridging the knowledge gap in AI-driven agricultural solutions for diverse linguistic communities.

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