  Large language models (LLMs) possess a wealth of knowledge encoded in their
parameters. However, this knowledge may become outdated or unsuitable over
time. As a result, there has been a growing interest in knowledge editing for
LLMs and evaluating its effectiveness. Existing studies primarily focus on
knowledge editing using factual triplets, which not only incur high costs for
collection but also struggle to express complex facts. Furthermore, these
studies are often limited in their evaluation perspectives. In this paper, we
propose Eva-KELLM, a new benchmark for evaluating knowledge editing of LLMs.
This benchmark includes an evaluation framework and a corresponding dataset.
Under our framework, we first ask the LLM to perform knowledge editing using
raw documents, which provides a more convenient and universal approach compared
to using factual triplets. We then evaluate the updated LLM from multiple
perspectives. In addition to assessing the effectiveness of knowledge editing
and the retention of unrelated knowledge from conventional studies, we further
test the LLM's ability in two aspects: 1) Reasoning with the altered knowledge,
aiming for the LLM to genuinely learn the altered knowledge instead of simply
memorizing it. 2) Cross-lingual knowledge transfer, where the LLM updated with
raw documents in one language should be capable of handling queries from
another language. To facilitate further research, we construct and release the
corresponding dataset. Using this benchmark, we investigate the effectiveness
of several commonly-used knowledge editing methods. Experimental results
indicate that the current methods for knowledge editing using raw documents are
not effective in yielding satisfactory results, particularly when it comes to
reasoning with altered knowledge and cross-lingual knowledge transfer.
