Recently, Knowledge Editing has received increasing attention, since it could
update the specific knowledge from outdated ones in pretrained models without
re-training. However, as pointed out by recent studies, existing related
methods tend to merely memorize the superficial word composition of the edited
knowledge, rather than truly learning and absorbing it. Consequently, on the
reasoning questions, we discover that existing methods struggle to utilize the
edited knowledge to reason the new answer, and tend to retain outdated
responses, which are generated by the original models utilizing original
knowledge. Nevertheless, the outdated responses are unexpected for the correct
answers to reasoning questions, which we named as the outdated issue. To
alleviate this issue, in this paper, we propose a simple yet effective decoding
strategy, i.e., outDated ISsue aware deCOding (DISCO), to enhance the
performance of edited models on reasoning questions. Specifically, we capture
the difference in the probability distribution between the original and edited
models. Further, we amplify the difference of the token prediction in the
edited model to alleviate the outdated issue, and thus enhance the model
performance w.r.t the edited knowledge. Experimental results suggest that
applying DISCO could enhance edited models to reason, e.g., on reasoning
questions, DISCO outperforms the prior SOTA method by 12.99 F1 scores, and
reduces the ratio of the outdated issue to 5.78% on the zsRE dataset.

我们提出了一种名为 DISCO 的简单而有效的解码策略，通过捕捉原始模型与编辑模型之间的概率分布差异，并增强编辑模型中的令牌预测差异，从而减轻过时问题，提高编辑模型在推理问题上的性能。