Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915421003710464 |
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| author | Kaur, Rishemjit Bhankhar, Arshdeep Singh Salh, Jashanpreet Singh Rajput, Sudhir Vidhi Mahendra, Kashish Berwal, Bhavika Kumar, Ritesh Ranathunga, Surangika |
| author_facet | Kaur, Rishemjit Bhankhar, Arshdeep Singh Salh, Jashanpreet Singh Rajput, Sudhir Vidhi Mahendra, Kashish Berwal, Bhavika Kumar, Ritesh Ranathunga, Surangika |
| contents | 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. Publicly available general-purpose Large Language Models (LLMs) typically offer generic agriculture advisories, lacking precision in local and multilingual contexts. Our study addresses this limitation by generating multilingual (English, Hindi, Punjabi) synthetic datasets from agriculture-specific documents from India and fine-tuning LLMs for the task of question answering (QA). Evaluation on human-created datasets demonstrates significant improvements in factuality, relevance, and agricultural consensus for the fine-tuned LLMs compared to the baseline counterparts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16974 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain Kaur, Rishemjit Bhankhar, Arshdeep Singh Salh, Jashanpreet Singh Rajput, Sudhir Vidhi Mahendra, Kashish Berwal, Bhavika Kumar, Ritesh Ranathunga, Surangika Computation and Language Artificial Intelligence I.2.7; J.m 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. Publicly available general-purpose Large Language Models (LLMs) typically offer generic agriculture advisories, lacking precision in local and multilingual contexts. Our study addresses this limitation by generating multilingual (English, Hindi, Punjabi) synthetic datasets from agriculture-specific documents from India and fine-tuning LLMs for the task of question answering (QA). Evaluation on human-created datasets demonstrates significant improvements in factuality, relevance, and agricultural consensus for the fine-tuned LLMs compared to the baseline counterparts. |
| title | Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain |
| topic | Computation and Language Artificial Intelligence I.2.7; J.m |
| url | https://arxiv.org/abs/2507.16974 |