Model Editing Can Hurt General Abilities of Large Language Models
(2401.04700)Abstract
One critical challenge that has emerged is the presence of hallucinations in the output of LLMs due to false or outdated knowledge. Since retraining LLMs with updated information is resource-intensive, there has been a growing interest in model editing. However, current model editing methods, while effective in improving editing performance in various scenarios, often overlook potential side effects on the general abilities of LLMs. In this paper, we raise concerns that model editing inherently improves the factuality of the model, but may come at the cost of a significant degradation of these general abilities. Systematically, we analyze side effects by evaluating four popular editing methods on three LLMs across eight representative task categories. Extensive empirical research reveals that current model editing methods are difficult to couple well with LLMs to simultaneously improve the factuality and maintain the general abilities such as reasoning, question answering, etc. Strikingly, the use of a specific method to edit LLaMA-1 (7B) resulted in a drastic performance degradation to nearly 0 on all selected tasks with just a single edit. Therefore, we advocate for more research efforts to minimize the loss of general abilities acquired during LLM pre-training and to ultimately preserve them during model editing.
Overview
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LLMs have impressive capabilities but can become outdated or produce errors, leading researchers to explore 'model editing' to fix inaccuracies without full retraining.
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Model editing is an active field with various methods developed; however, its impact on the overall abilities of LLMs has been understudied.
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This paper examines four model editing methods—KN, MEND, ROME, and MEMIT—across two LLMs and eight task categories to assess their effects.
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Editing can successfully update information, but this paper finds that it often diminishes performance in other, non-targeted tasks.
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The paper concludes with a warning about the trade-offs involved in model editing and the need for further research to preserve LLMs' general capabilities while enhancing factual accuracy.
Introduction
Artificial intelligence has made significant leaps in recent years, particularly in the realm of NLP. LLMs—LLMs—have shown remarkable abilities to generate text, answer questions, and even create poetry. But as powerful as these models are, their knowledge is frozen at the point they were last trained. They can produce content that is outdated or just plain wrong, something that's referred to as "hallucinations." While retraining models with updated information is the ideal solution, doing so requires substantial computational resources. To address this, researchers have turned to "model editing," a process that involves tweaking a model's details to correct misinformation. However, a growing body of research suggests that these edits, while correcting one issue, might be deteriorating the overall abilities of LLMs.
Related Work
Model editing is an active area of research, aiming to keep LLMs accurate without the need for full retraining. Methods for model editing have evolved, with some focusing on memory-based approaches that work outside the model, while others employ meta-learning techniques or pinpoint and edit specific neurons associated with particular facts. Despite advancements, till now, the predominant focus has been on improving the editing performance specifically, often paying less attention to the broader impact on the model's abilities.
Evaluating Model Editing
This paper critically examines four popular model editing methods across eight task categories within two different LLMs. The methods—KN, MEND, ROME, and MEMIT—vary in their approach to editing, from modifying key neurons to using a hypernetwork that adjusts model weights based on specific inputs. Assessments include a variety of editing scenarios, like instance vs. batch editing, and single vs. sequential editing. The findings are profound, showing that while editing can effectively update specific pieces of information, it often results in a drop in performance on other general tasks such as question answering and sentiment analysis.
Conclusion and Discussion
The conclusion is cautionary: while model editing is a seemingly promising direction, there is a trade-off in the general task performance of LLMs. The analysis reveals a concerning trend of performance degradation with increase in editing operations or batch sizes, indicating that current editing techniques may be undermining the robustness of these models. The paper calls for future research to refine model editing methods, aiming to preserve the breadth of LLMs' capabilities while enhancing their factual accuracy. This is not just about improving individual tasks, but about ensuring the sustainable development of LLMs as reliable, general-purpose tools for various applications.
In this rapidly evolving field, the ongoing challenge is to balance the need for accurate, up-to-date information with the imperative to maintain a model's general abilities. Only with continued scrutiny and innovative approaches can the full potential of AI be harnessed responsibly.
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