Can Knowledge Editing Really Correct Hallucinations?

Large Language Models (LLMs) suffer from hallucinations, referring to the nonfactual information in generated content, despite their superior capacities across tasks. Meanwhile, knowledge editing has been developed as a new popular paradigm to correct the erroneous factual knowledge encoded in LLMs with the advantage of avoiding retraining from scratch. However, one common issue of existing evaluation datasets for knowledge editing is that they do not ensure LLMs actually generate hallucinated answers to the evaluation questions before editing. When LLMs are evaluated on such datasets after being edited by different techniques, it is hard to directly adopt the performance to assess the effectiveness of different knowledge editing methods in correcting hallucinations. Thus, the fundamental question remains insufficiently validated: Can knowledge editing really correct hallucinations in LLMs? We proposed HalluEditBench to holistically benchmark knowledge editing methods in correcting real-world hallucinations. First, we rigorously construct a massive hallucination dataset with 9 domains, 26 topics and more than 6, 000 hallucinations. Then, we assess the performance of knowledge editing methods in a holistic way on five dimensions including Efficacy, Generalization, Portability, Locality, and Robustness. Through HalluEditBench, we have provided new insights into the potentials and limitations of different knowledge editing methods in correcting hallucinations, which could inspire future improvements and facilitate the progress in the field of knowledge editing. Considering Table 1: Performance measured by Accuracy (%) the high cost of retraining LLMs from scratch, of Llama2-7B before editing ("Pre-edit") and after knowledge editing has been designed as a new applying typical knowledge editing methods ("Postedit") paradigm to correct erroneous or outdated factual on common existing evaluation datasets. When such datasets are adopted to evaluate the performance of LLMs after being edited, it is hard to directly use the scores to judge the effectiveness of different knowledge editing techniques in correcting hallucinations, which is the motivation of applying knowledge editing to LLMs. To better illustrate this point, following the evaluation setting in (Zhang et al., 2024e), we conducted a preliminary study to examine the pre-edit and post-edit performances of Llama2-7B on the aforementioned Who is the Chief Scientist of OpenAI? Who is the Chief Scientist of OpenAI? Who is the Chief Scientist of OpenAI?