We propose an approach for estimating the latent knowledge embedded inside
large language models (LLMs). We leverage the in-context learning (ICL)
abilities of LLMs to estimate the extent to which an LLM knows the facts stored
in a knowledge base. Our knowledge estimator avoids reliability concerns with
previous prompting-based methods, is both conceptually simpler and easier to
apply, and we demonstrate that it can surface more of the latent knowledge
embedded in LLMs. We also investigate how different design choices affect the
performance of ICL-based knowledge estimation. Using the proposed estimator, we
perform a large-scale evaluation of the factual knowledge of a variety of open
source LLMs, like OPT, Pythia, Llama(2), Mistral, Gemma, etc. over a large set
of relations and facts from the Wikidata knowledge base. We observe differences
in the factual knowledge between different model families and models of
different sizes, that some relations are consistently better known than others
but that models differ in the precise facts they know, and differences in the
knowledge of base models and their finetuned counterparts.

我们提出了一种估计大型语言模型中嵌入的潜在知识的方法，该方法利用了大型语言模型的上下文学习能力来估计模型对存储在知识库中的事实的了解程度，并且我们的知识估计器避免了以前的提示式方法中存在的可靠性问题，且在概念上更简单且更易于应用，我们证明它可以揭示出更多嵌入在大型语言模型中的潜在知识。我们还研究了不同设计选择对基于上下文学习的知识估计性能的影响。通过所提出的估计器，我们对各种开源大型语言模型（如 OPT，Pythia，Llama (2)，Mistral，Gemma 等）在来自 Wikidata 知识库的大量关系和事实上的事实知识进行了大规模评估。我们观察到不同模型家族和不同大小的模型之间的事实知识存在差异，某些关系普遍比其他关系更为熟知，但是模型在知识上存在差异，以及基本模型和其微调模型之间的知识差异。

LLMs 中可靠的潜在知识估计：与上下文学习相比，基于提示的事实知识提取

Towards Reliable Latent Knowledge Estimation in LLMs: In-Context  Learning vs. Prompting Based Factual Knowledge Extraction

Recent work has presented intriguing results examining the knowledge
contained in language models (LM) by having the LM fill in the blanks of
prompts such as "Obama is a _ by profession". These prompts are usually
manually created, and quite possibly sub-optimal; another prompt such as "Obama
worked as a _" may result in more accurately predicting the correct profession.
Because of this, given an inappropriate prompt, we might fail to retrieve facts
that the LM does know, and thus any given prompt only provides a lower bound
estimate of the knowledge contained in an LM. In this paper, we attempt to more
accurately estimate the knowledge contained in LMs by automatically discovering
better prompts to use in this querying process. Specifically, we propose
mining-based and paraphrasing-based methods to automatically generate
high-quality and diverse prompts, as well as ensemble methods to combine
answers from different prompts. Extensive experiments on the LAMA benchmark for
extracting relational knowledge from LMs demonstrate that our methods can
improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what
LMs know. We have released the code and the resulting LM Prompt And Query
Archive (LPAQA) at this https URL

本文提出使用基于挖掘和释义的自动化方法来生成高质量且多样化的提示语，并使用模型集成的方法来改进语言模型（LM）中的关系知识的查询。通过 LAMA 基准测试表明，该方法将 LM 的准确性从 31.1% 提升至 39.6%，为 LM 的知识提供了更严格的下限估计。