Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them. While Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets, their effectiveness on domain specific datasets remains underexplored. To address this gap, we investigate the potential of LLMs as efficient data annotators for extracting relations in financial documents. We compare the annotations produced by three LLMs (GPT-4, PaLM 2, and MPT Instruct) against expert annotators and crowdworkers. We demonstrate that the current state-of-the-art LLMs can be sufficient alternatives to non-expert crowdworkers. We analyze models using various prompts and parameter settings and find that customizing the prompts for each relation group by providing specific examples belonging to those groups is paramount. Furthermore, we introduce a reliability index (LLM-RelIndex) used to identify outputs that may require expert attention. Finally, we perform an extensive time, cost and error analysis and provide recommendations for the collection and usage of automated annotations in domain-specific settings.

As electronics get smaller and manufacturers come under greater pressure to improve efficiency and throughput, the traditional hand soldering method is no longer up to scratch. In 1896, a patent for electric heating apparatus, now commonly known as a soldering iron, was granted. The process of soldering has remained much the same since then. But, that's about to change. In this article, Nigel Smith, CEO of TM Robotics, international distributor of Shibaura Machine, formerly Toshiba Machine, industrial and soldering robots, explains three reasons why you should automate the soldering process.