Ideally, dialogue systems should generate responses that are faithful to the
knowledge contained in relevant documents. However, many models generate
hallucinated responses instead that contradict it or contain unverifiable
information. To mitigate such undesirable behaviour, it has been proposed to
fine-tune a `negative expert' on negative examples and subtract its parameters
from those of a pre-trained model. However, intuitively, this does not take
into account that some parameters are more responsible than others in causing
hallucinations. Thus, we propose to weigh their individual importance via (an
approximation of) the Fisher Information matrix, which measures the uncertainty
of their estimate. We call this method Elastic Weight Removal (EWR). We
evaluate our method -- using different variants of Flan-T5 as a backbone
language model -- on multiple datasets for information-seeking dialogue
generation and compare our method with state-of-the-art techniques for
faithfulness, such as CTRL, Quark, DExperts, and Noisy Channel reranking.
Extensive automatic and human evaluation shows that EWR systematically
increases faithfulness at minor costs in terms of other metrics. However, we
notice that only discouraging hallucinations may increase extractiveness, i.e.
shallow copy-pasting of document spans, which can be undesirable. Hence, as a
second main contribution, we show that our method can be extended to
simultaneously discourage hallucinations and extractive responses. We publicly
release the code for reproducing EWR and all baselines.

提出了一种称为弹性权重剔除（EWR）的方法，该方法通过测量权重的不确定性来约束预先训练的模型参数，使得对话系统生成关于相关文件的信息时，不会产生错误或不可验证的信息。该方法使用 Flan-T5 作为主干语言模型，通过自动和人工评估表明，它在提高忠诚度方面具有系统性的优势。同时该方法还可以同时限制幻觉和提取响应，以防止浅层的复制和粘贴文档范围的情况。