Several recent papers have investigated the potential of language models as
knowledge bases as well as the existence of severe biases when extracting
factual knowledge. In this work, we focus on the factual probing performance
over unseen prompts from tuning, and using a probabilistic view we show the
inherent misalignment between pre-training and downstream tuning objectives in
language models for probing knowledge. We hypothesize that simultaneously
debiasing these objectives can be the key to generalisation over unseen
prompts. We propose an adapter-based framework, UniArk, for generalised and
consistent factual knowledge extraction through simple methods without
introducing extra parameters. Extensive experiments show that UniArk can
significantly improve the model's out-of-domain generalisation as well as
consistency under various prompts. Additionally, we construct ParaTrex, a
large-scale and diverse dataset for measuring the inconsistency and
out-of-domain generation of models. Further, ParaTrex offers a reference method
for constructing paraphrased datasets using large language models.

使用适配器框架 UniArk，通过简单方法进行广义和一致的事实知识提取，大幅提高了模型在领域外的概括能力和多种提示下的一致性。此外，构建了 ParaTrex，一个大规模且多样化的数据集，用于测量模型的不一致性和领域外生成，并提供了使用大型语言模型构建改写数据集的参考方法。

UniArk: 去偏见提高事实知识抽取的泛化能力和一致性

UniArk: Improving Generalisation and Consistency for Factual Knowledge  Extraction through Debiasing

The current paradigm of large-scale pre-training and fine-tuning Transformer
large language models has lead to significant improvements across the board in
natural language processing. However, such large models are susceptible to
overfitting to their training data, and as a result the models perform poorly
when the domain changes. Also, due to the model's scale, the cost of
fine-tuning the model to the new domain is large. Nonparametric Variational
Information Bottleneck (NVIB) has been proposed as a regulariser for training
cross-attention in Transformers, potentially addressing the overfitting
problem. We extend the NVIB framework to replace all types of attention
functions in Transformers, and show that existing pretrained Transformers can
be reinterpreted as Nonparametric Variational (NV) models using a proposed
identity initialisation. We then show that changing the initialisation
introduces a novel, information-theoretic post-training regularisation in the
attention mechanism, which improves out-of-domain generalisation without any
training. This success supports the hypothesis that pretrained Transformers are
implicitly NV Bayesian models.

当前大规模预训练和微调 Transformer 大型语言模型的范式在自然语言处理方面取得了显著的进展，然而这种大型模型容易在训练数据上过拟合，在领域变化时性能表现差，而将非参数变分信息瓶颈（NVIB）框架扩展到 Transformers 的所有类型的注意力函数可以解决过拟合问题，改变初始化可以引入一种新的基于信息论的后训练正则化的注意机制，提高领域外泛化能力，这证实了预训练的 Transformer 模型隐式地是 NV 贝叶斯模型。