  Large language models (LLMs) can make predictions using parametric
knowledge--knowledge encoded in the model weights--or contextual
knowledge--knowledge presented in the context. In many scenarios, a desirable
behavior is that LLMs give precedence to contextual knowledge when it conflicts
with the parametric knowledge, and fall back to using their parametric
knowledge when the context is irrelevant. This enables updating and correcting
the model's knowledge by in-context editing instead of retraining. Previous
works have shown that LLMs are inclined to ignore contextual knowledge and fail
to reliably fall back to parametric knowledge when presented with irrelevant
context. In this work, we discover that, with proper prompting methods,
instruction-finetuned LLMs can be highly controllable by contextual knowledge
and robust to irrelevant context. Utilizing this feature, we propose EREN (Edit
models by REading Notes) to improve the scalability and robustness of LLM
editing. To better evaluate the robustness of model editors, we collect a new
dataset, that contains irrelevant questions that are more challenging than the
ones in existing datasets. Empirical results show that our method outperforms
current state-of-the-art methods by a large margin. Unlike existing techniques,
it can integrate knowledge from multiple edits, and correctly respond to
syntactically similar but semantically unrelated inputs (and vice versa). The
source code can be found at https://github.com/thunlp/EREN.
