  Recent work (e.g. LAMA (Petroni et al., 2019)) has found that the quality of
the factual information extracted from Large Language Models (LLMs) depends on
the prompts used to query them. This inconsistency is problematic because
different users will query LLMs for the same information using different
wording, but should receive the same, accurate responses regardless. In this
work we aim to address this shortcoming by introducing P-Adapters: lightweight
models that sit between the embedding layer and first attention layer of LLMs.
They take LLM embeddings as input and output continuous prompts that are used
to query the LLM. Additionally, we investigate Mixture of Experts (MoE) models
that learn a set of continuous prompts ("experts") and select one to query the
LLM. They require a separate classifier trained on human-annotated data to map
natural language prompts to the continuous ones. P-Adapters perform comparably
to the more complex MoE models in extracting factual information from BERT and
RoBERTa while eliminating the need for additional annotations. P-Adapters show
between 12-26% absolute improvement in precision and 36-50% absolute
improvement in consistency over a baseline of only using natural language
queries. Finally, we investigate what makes P-Adapters successful and conclude
that a significant factor is access to the LLM's embeddings of the original
natural language prompt, particularly the subject of the entity pair being
queried.
