Emptying the Ocean with a Spoon: Should We Edit Models? (2310.11958v1)

Abstract: 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.

An Evaluation of Direct Model Editing in LLMs

In "Emptying the Ocean with a Spoon: Should We Edit Models?" Pinter and Elhadad critically assess the emerging practice of direct model editing as a method to correct factual inaccuracies in the outputs of LLMs. The authors scrutinize this approach by contrasting it with other methodologies that tackle the challenge of factual consistency through retrieval-based architectures, concept erasure, and attribution techniques. This essay explores the central arguments of the paper, highlighting the concerns related to model editing, while also discussing promising alternatives.

Core Criticisms of Model Editing

The paper outlines several incontrovertible issues with the direct editing of models. Firstly, Pinter and Elhadad challenge the premise that LLMs can function as reliable repositories of factual information. They illustrate the inherent misalignment between LLMs' stochastic nature and their use as factual resources, given that these models produce outputs based on learned distributions of language rather than verified knowledge. This raises critical questions about the suitability of treating LLMs as fact-banks that can be simply updated via direct parameter modification.

The systemic mismatch is further compounded by straightforward architectural challenges. With the ever-expanding ocean of facts, the task of manually editing a limited set of parameters to reflect the current truth is deemed infeasible and impractical. Such efforts may also introduce biases, potentially neglecting less popular facts during updates, which aggravates the risk of retaining or encroaching systemic inaccuracies.

Additionally, the complexity of maintaining logical consistency across related facts further complicates the editing task. This points to a broader issue: the theoretical underpinnings that render systematic model editing a computationally intricate, if not theoretically intractable, venture.

Alternative Approaches to Ensuring Factual Consistency

Despite being highly critical of direct model editing, Pinter and Elhadad recognize that alternative methodologies could offer more robust solutions:

1. Retrieval-Based Architectures: These systems decouple the storage of factual knowledge from the LLMs themselves, employing external knowledge bases that can be updated independently. Concepts such as k-nearest neighbor approaches, RETRO-style architectures, and others leverage retrieval to incorporate accurate information dynamically without altering the model's internal parameters.
2. Continual Training and Updating: Building a bridge between new tasks and existing ones via continuous learning paradigms can facilitate a more holistic update mechanism that incorporates new knowledge while preserving existing capabilities.
3. Concept Erasure Techniques: While not directly applicable to factual updates, concept erasure addresses specific unwanted biases through post-hoc embedding transformations. This field may offer insights for erasing outdated or incorrect facts without compromising the model integrity.
4. Acknowledging Unknowns: Developing mechanisms for LLMs to recognize and indicate uncertainty or lack of knowledge in response to particular queries could aid in deploying these models responsibly, avoiding unwarranted reliance on potentially incorrect outputs.

Implications and Future Directions

The authors urge caution in the deployment of LLMs, particularly in settings that demand high factual accuracy. They advocate for the responsible promotion of LLMs in applications that do not depend on model editing, thereby mitigating possible overreliance on their perceived factual reliability. Furthermore, they recommend focusing on combining LLM capabilities with external, contextually appropriate, and reliable knowledge sources.

Looking ahead, further exploration into hybrid architectures that integrate retrieval-based approaches with LLM generative strengths seems promising. This combined strategy may offer a pathway to harness the full potential of LLMs by ensuring factual robustness without resorting to direct model editing. Equipping models with the ability to transparently cite sources or acknowledge their own limitations also stands out as a significant research direction in enhancing the trustworthiness of AI systems.

In conclusion, Pinter and Elhadad present a compelling critique of direct model editing, underscoring its theoretical and practical limitations. Their analysis is a crucial call to action for the AI research community to pursue alternative strategies that ensure both the robustness and reliability of LLMs in knowledge-dependent applications.