Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
On the map: Synthetic data and self-improvement for LLMs → Synthetic reasoning data and bootstrapping
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Synthetic reasoning data and bootstrapping, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 18 of its 51 references that are in the map. First posted on arXiv 22 Jul 2025.
Builds on
18 of 51 references.
References · 18
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6 more
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- 2024SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages