“Deploying tuning-free prompting is challenging in engineering practice: it not only requiresusers to engage in cumbersome trials and errors but is also extremely time-consuming,as even a slight change in wording and phrasing could have a huge impact on the finalperformance. To further investigate the impact of different prompts, in this work, weperform a systematic inspection of four factors in linguistics involved in prompt engineering:syntax, semantics, lexicon, and pragmatics. The empirical results quantify the sensitivityof the output to small textual perturbations in four linguistic factors of prompts. Basedon the analysis of these four factors, we present a series of design guidelines to helphuman users write effective prompts. Human evaluation on amateurs shows that usingthe proposed guidelines helps humans produce prompts with significant gains in zero-shotperformance in Pre-trained Language Models (PLMs) and hence validates the utility ofthe guidelines.”

In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing IE toolkits have several non-trivial problems, such as not supporting multi-tasks, and not supporting automatic updates. In this work, we present CollabKG, a learnable human-machine-cooperative IE toolkit for KG and EKG construction. Specifically, for the multi-task issue, CollabKG unifies different IE subtasks, including named entity recognition (NER), entity-relation triple extraction (RE), and event extraction (EE), and supports both KG and EKG. Then, combining advanced prompting-based IE technology, the human-machine-cooperation mechanism with Large Language Models (LLMs) as the assistant machine is presented which can provide a lower cost as well as a higher performance. Lastly, owing to the two-way interaction between the human and machine, CollabKG with learning ability allows self-renewal. Besides, CollabKG has several appealing features (e.g., customization, training-free, and label propagation) that make the system powerful and high-productivity. We holistically compare our toolkit with other existing tools on these features. Human evaluation quantitatively illustrates that CollabKG significantly improves annotation quality, efficiency, and stability simultaneously.