Large Language Model (LLM) editing modifies factual information in LLMs.
Locate-and-Edit (L\&E) methods accomplish this by finding where relevant
information is stored within the neural network, and editing the weights at
that location. The goal of editing is to modify the response of an LLM to a
proposition independently of its phrasing, while not modifying its response to
other related propositions. Existing methods are limited to binary
propositions, which represent straightforward binary relations between a
subject and an object. Furthermore, existing methods rely on semantic subject
labels, which may not be available or even be well-defined in practice. In this
paper, we show that both of these issues can be effectively skirted with a
simple and fast localization method called Gradient Tracing (GT). This
localization method allows editing arbitrary propositions instead of just
binary ones, and does so without the need for subject labels. As propositions
always have a truth value, our experiments prompt an LLM as a boolean
classifier, and edit its T/F response to propositions. Our method applies GT
for location tracing, and then edit the model at that location using a mild
variant of Rank-One Model Editing (ROME). On datasets of binary propositions
derived from the CounterFact dataset, we show that our method -- without access
to subject labels -- performs close to state-of-the-art L\&E methods which has
access subject labels. We then introduce a new dataset, Factual Accuracy
Classification Test (FACT), which includes non-binary propositions and for
which subject labels are not generally applicable, and therefore is beyond the
scope of existing L\&E methods. Nevertheless, we show that with our method
editing is possible on FACT.

使用梯度追踪（GT）和一种简单快速的定位方法，本文介绍了一种新的大语言模型（LLM）编辑方法，使其能编辑任意命题而不仅仅是二元命题，并且无需主语标签，实验结果表明该方法在无主语标签的情况下也能达到接近最先进的方法的编辑性能；另外，还引入了一个新的数据集，名为 Factual Accuracy Classification Test（FACT），该数据集包含非二元命题，对于非通用主语标签的情况，显示出了我们的方法可以在 FACT 上进行编辑。