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P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
[article]
2022
arXiv
pre-print
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
arXiv:2110.07280v2
fatcat:f347bvfmsngzpgeztzomsxg744