Recent advancements in Large Language Models (LLMs) have showcased exceptional performance in zero-shot learning and reasoning tasks. However, integrating these models with external tools - a crucial need for real-world applications - remains a significant challenge. We propose RESTful-Llama, a novel framework designed to enable Llama 3.1 to transform natural language instructions into effective RESTful API calls. To enhance the fine-tuning process, we introduce DOC_Mine, a method to generate fine-tuning datasets from public API documentation. RESTful-Llama distinguishes itself by enabling open-source LLMs to efficiently interact with and adapt to any REST API system. Experiments demonstrate a 31.9% improvement in robustness and a 2.33x increase in efficiency compared to existing methods.

Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries. The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.