An Empirical Study on Few-shot Knowledge Probing for Pretrained Language
  Models
  Prompt-based knowledge probing for 1-hop relations has been used to measure
how much world knowledge is stored in pretrained language models. Existing work
uses considerable amounts of data to tune the prompts for better performance.
In this work, we compare a variety of approaches under a few-shot knowledge
probing setting, where only a small number (e.g., 10 or 20) of example triples
are available. In addition, we create a new dataset named TREx-2p, which
contains 2-hop relations. We report that few-shot examples can strongly boost
the probing performance for both 1-hop and 2-hop relations. In particular, we
find that a simple-yet-effective approach of finetuning the bias vectors in the
model outperforms existing prompt-engineering methods. Our dataset and code are
available at \url{https://github.com/cloudygoose/fewshot_lama}.
