Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
Abstrak
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.
Penulis (9)
Rishemjit Kaur
Arshdeep Singh Bhankhar
Jashanpreet Singh Salh
Sudhir Rajput
Vidhi
Kashish Mahendra
Bhavika Berwal
Ritesh Kumar
Surangika Ranathunga
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓