In-Context Editing: Learning Knowledge from Self-Induced Distributions

Abstract: In scenarios where LLMs must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded generalization, and unnatural language generation. To address these limitations, we introduce Consistent In-Context Editing (ICE), a novel approach leveraging the model's in-context learning capability to optimize toward a contextual distribution rather than a one-hot target. ICE introduces a simple yet effective optimization framework for the model to internalize new knowledge by aligning its output distributions with and without additional context. This method enhances the robustness and effectiveness of gradient-based tuning methods, preventing overfitting and preserving the model's integrity. We analyze ICE across four critical aspects of knowledge editing: accuracy, locality, generalization, and linguistic quality, demonstrating its advantages. Experimental results confirm the effectiveness of ICE and demonstrate its potential for continual editing, ensuring that the integrity of the model is preserved while updating information.

• The paper presents ICE, a novel method that leverages in-context learning to update language models without overfitting.
• It demonstrates enhanced accuracy, locality, generalization, and linguistic quality across four datasets.
• ICE is effective in continual editing scenarios, dynamically updating models while preserving existing knowledge.

The paper "In-Context Editing: Learning Knowledge from Self-Induced Distributions" (2406.11194) presents a novel methodology for updating information in LLMs without the usual pitfalls of existing fine-tuning approaches. Traditional fine-tuning methods often lead to overfitting, degraded performance, and unnatural text generation when tasked with knowledge editing. To alleviate these issues, the authors introduce Consistent In-Context Editing (ICE).

ICE leverages the in-context learning capabilities of LLMs to adapt to a contextual distribution instead of relying on a rigid one-hot target. This approach is implemented through a straightforward optimization framework that includes both specific targets and procedural guidelines, leading to more robust and effective tuning of the model through gradient-based methods.

The paper provides an analytical examination of ICE covering four pivotal aspects of knowledge editing: accuracy, locality, generalization, and linguistic quality. The authors argue that ICE substantially improves these dimensions compared to previous methods. Experimental validations conducted across four datasets reveal that ICE not only enhances the reliability of updates but also maintains the overall integrity of the model.

The results show that ICE is particularly promising for continual editing scenarios, where it effectively incorporates updated information while preserving the model's existing knowledge base. This innovation holds potential for applications requiring dynamic knowledge updates without extensive retraining, offering a more versatile and resilient alternative to conventional fine-tuning paradigms.