Where It Really Matters: Few-Shot Environmental Conservation Media Monitoring for Low-Resource Languages
It addresses the critical need for scalable AI tools in environmental conservation for low-resource language communities, where expert annotation is scarce, though it is incremental as it builds on existing LLM and few-shot techniques.
The paper tackles the problem of automated media monitoring for environmental conservation in low-resource languages, where existing systems require large labeled datasets that are infeasible. It proposes NewsSerow, a method using LLMs with few-shot learning, which outperforms other few-shot methods and matches fully fine-tuned models using thousands of examples, with deployments in Nepal and Colombia reducing operational burden.
Environmental conservation organizations routinely monitor news content on conservation in protected areas to maintain situational awareness of developments that can have an environmental impact. Existing automated media monitoring systems require large amounts of data labeled by domain experts, which is only feasible at scale for high-resource languages like English. However, such tools are most needed in the global south where news of interest is mainly in local low-resource languages, and far fewer experts are available to annotate datasets sustainably. In this paper, we propose NewsSerow, a method to automatically recognize environmental conservation content in low-resource languages. NewsSerow is a pipeline of summarization, in-context few-shot classification, and self-reflection using large language models (LLMs). Using at most 10 demonstration example news articles in Nepali, NewsSerow significantly outperforms other few-shot methods and achieves comparable performance with models fully fine-tuned using thousands of examples. The World Wide Fund for Nature (WWF) has deployed NewsSerow for media monitoring in Nepal, significantly reducing their operational burden, and ensuring that AI tools for conservation actually reach the communities that need them the most. NewsSerow has also been deployed for countries with other languages like Colombia.