A few-shot learning approach for a multilingual agro-information question answering system

Abstract

Agriculture plays a crucial role in numerous households across Sub-Saharan Africa. Developing a question answering system that utilizes agricultural expertise and agro-information can effectively bridge the support gap for farmers in the local community. Most advances in question answering research involve large language models trained on extensive data. Nevertheless, the conventional approach of fine-tuning has demonstrated a significant decline in performance when models are fine-tuned on a small amount of data. This decline is primarily attributed to the disparities between the objectives of pretraining and fine-tuning. One proposed alternative is to utilize prompt-based fine-tuning, which permits the model to be fine-tuned with only a few examples. Extensive research has been done on the application of these methods to tasks such as text classification and not question answering. This research aims to study the feasibility of recent fewshot learning approaches, such as FewshotQA and Null prompting, for domain-specific agricultural data in 4 South African languages. We evaluated the overall performance of these approaches and investigated the effects of adapting these approaches for cross-lingual extractive question answering of domain-specific data. The results obtained in this study have shown valuable insight into the applicability of these methods to domain-specific data. These results have shown that these methods are capable of adequately capturing the textual information of domain-specific data from the initial subset of data points. Thus, there is potential for using these methods as a practical solution for limited data.