We call into question the recently popularized method of direct model editing as a means of correcting factual errors in LLM generations. We contrast model editing with three similar but distinct approaches that pursue better defined objectives: (1) retrieval-based architectures, which decouple factual memory from inference and linguistic capabilities embodied in LLMs; (2) concept erasure methods, which aim at preventing systemic bias in generated text; and (3) attribution methods, which aim at grounding generations into identified textual sources. We argue that direct model editing cannot be trusted as a systematic remedy for the disadvantages inherent to LLMs, and while it has proven potential in improving model explainability, it opens risks by reinforcing the notion that models can be trusted for factuality. We call for cautious promotion and application of model editing as part of the LLM deployment process, and for responsibly limiting the use cases of LLMs to those not relying on editing as a critical component.

质疑直接模型编辑方法在修正LLM生成的事实错误方面的有效性，提出与其类似但不同的三种方法：基于检索的体系结构、概念擦除方法和归因方法，认为直接模型编辑不能作为LLMs内在缺陷的系统性疗法，尽管在提高模型可解释性方面具有潜力，但通过强调模型的可信性而带来风险，呼吁谨慎推广和应用模型编辑作为LLM部署过程的一部分，并负责任地将LLMs的用例限制在不依赖编辑作为关键组成部分的情况下。

用勺子挖空海洋：我们应该编辑模型吗？