Knowledge Updating? No More Model Editing! Just Selective Contextual Reasoning (2503.05212v1)

Abstract: As real-world knowledge evolves, the information embedded within LLMs can become outdated, inadequate, or erroneous. Model editing has emerged as a prominent approach for updating LLMs' knowledge with minimal computational costs and parameter changes. This approach typically identifies and adjusts specific model parameters associated with newly acquired knowledge. However, existing methods often underestimate the adverse effects that parameter modifications can have on broadly distributed knowledge. More critically, post-edit LLMs frequently struggle with multi-hop reasoning and continuous knowledge updates. Although various studies have discussed these shortcomings, there is a lack of comprehensive evaluation. In this paper, we provide an evaluation of ten model editing methods along four dimensions: reliability, generalization, locality, and portability. Results confirm that all ten popular model editing methods show significant shortcomings across multiple dimensions, suggesting model editing is less promising. We then propose a straightforward method called Selective Contextual Reasoning (SCR), for knowledge updating. SCR does not modify model parameters but harnesses LLM's inherent contextual reasoning capabilities utilizing the updated knowledge pieces. Under SCR, an LLM first assesses whether an incoming query falls within the scope of an external knowledge base. If it does, the relevant external knowledge texts are contextualized to enhance reasoning; otherwise, the query is answered directly. We evaluate SCR against the ten model editing methods on two counterfactual datasets with three backbone LLMs. Empirical results confirm the effectiveness and efficiency of contextual reasoning for knowledge updating.

Knowledge Updating through Selective Contextual Reasoning: A Reassessment of Model Editing

The paper "Knowledge Updating? No More Model Editing! Just Selective Contextual Reasoning" by He, Song, and Sun offers a critical examination of model editing approaches for knowledge updating in LLMs. It posits a novel strategy termed Selective Contextual Reasoning (SCR) as a more effective and efficient alternative, steering away from the parameter alteration strategies typical of model editing.

Evaluation of Model Editing Methods

Model editing has been a conventional approach to update knowledge within LLMs without retraining from scratch. However, these methods often face substantial challenges, such as maintaining the integrity and broad capabilities of the model after updates. Ten model editing methods—spanning locate-then-edit, fine-tuning, meta-learning, memory-based, and representation editing—were assessed on four dimensions: reliability, generalization, locality, and portability.

The paper highlights that current model editing methods face significant shortcomings. They particularly struggle with retaining reliability in multi-step updates, causing disruptions in the broader reasoning capabilities of LLMs. While some methods like GRACE and WISE performed adeptly in specific dimensions, none excelled across all four, often achieving improvements in one aspect at the expense of another. Furthermore, these methods were observed to lead to catastrophic forgetting when subjected to frequent updates or large knowledge shifts.

Introduction of Selective Contextual Reasoning (SCR)

The authors propose SCR as a streamlined alternative to these traditional editing methods. Instead of adjusting model parameters, SCR leverages LLMs' inherent contextual reasoning capabilities, relying on a two-step knowledge selection process: (1) semantic filtering using similarity-based retrieval from an expandable textual knowledge base and (2) LLM-based confirmation of relevance for final knowledge selection. This selected information is then appended to prompts for contextual enhancement during inference.

SCR excels by preserving model integrity and capitalizing on LLMs' strengths in contextual synthesis and reasoning. The method dynamically adapts external, real-time knowledge without altering the model’s embedded representations, facilitating both efficiency and an enhanced capacity for reasoning with newly updated information.

Experimental Results and Observations

The empirical evaluation conducted with multiple LLMs, including Llama-2, Llama-3.1, and Mistral, indicates that SCR markedly surpasses existing methods in balancing all four dimensions of knowledge updating. It achieves sustained performance across sequential edits without degradation, showcasing notable resilience against disruptions.

The results underscore that SCR achieves high accuracy in reliability, generalization, and especially in portability—a dimension where traditional methods faltered significantly. Moreover, SCR achieves these results with a simple implementation, requiring no additional training or complex parameter adjustments, and significantly lower computational cost.

Implications and Future Considerations

The findings imply significant practical implications for continuous knowledge updates in LLMs, emphasizing the need to shift from disruptive model editing to more adaptive contextual reasoning approaches. With continuous growth in LLM capabilities, SCR represents an efficient methodology that sidesteps the pitfalls of traditional model editing.

Theoretically, SCR suggests a new paradigm for integrating real-time knowledge updates, indicating that leveraging inherent LLM capabilities can lead to more robust knowledge systems. This approach could serve as a precursor for developing more advanced models that inherently balance stability and adaptability.

Future research may explore extending SCR frameworks, such as improving retrieval mechanisms for better knowledge selection or integrating more sophisticated reasoning algorithms. This could further refine the seamless integration of updated knowledge, maintaining LLMs’ holistic capabilities while swiftly adapting to dynamic knowledge landscapes.