  The increasing threat of disinformation calls for automating parts of the
fact-checking pipeline. Identifying text segments requiring fact-checking is
known as claim detection (CD) and claim check-worthiness detection (CW), the
latter incorporating complex domain-specific criteria of worthiness and often
framed as a ranking task. Zero- and few-shot LLM prompting is an attractive
option for both tasks, as it bypasses the need for labeled datasets and allows
verbalized claim and worthiness criteria to be directly used for prompting. We
evaluate the LLMs' predictive and calibration accuracy on five CD/CW datasets
from diverse domains, each utilizing a different worthiness criterion. We
investigate two key aspects: (1) how best to distill factuality and worthiness
criteria into a prompt and (2) what amount of context to provide for each
claim. To this end, we experiment with varying the level of prompt verbosity
and the amount of contextual information provided to the model. Our results
show that optimal prompt verbosity is domain-dependent, adding context does not
improve performance, and confidence scores can be directly used to produce
reliable check-worthiness rankings.
