Editing Factual Knowledge in Language Models (2104.08164v2)

Abstract: The factual knowledge acquired during pre-training and stored in the parameters of LLMs (LMs) can be useful in downstream tasks (e.g., question answering or textual inference). However, some facts can be incorrectly induced or become obsolete over time. We present KnowledgeEditor, a method which can be used to edit this knowledge and, thus, fix 'bugs' or unexpected predictions without the need for expensive re-training or fine-tuning. Besides being computationally efficient, KnowledgeEditordoes not require any modifications in LM pre-training (e.g., the use of meta-learning). In our approach, we train a hyper-network with constrained optimization to modify a fact without affecting the rest of the knowledge; the trained hyper-network is then used to predict the weight update at test time. We show KnowledgeEditor's efficacy with two popular architectures and knowledge-intensive tasks: i) a BERT model fine-tuned for fact-checking, and ii) a sequence-to-sequence BART model for question answering. With our method, changing a prediction on the specific wording of a query tends to result in a consistent change in predictions also for its paraphrases. We show that this can be further encouraged by exploiting (e.g., automatically-generated) paraphrases during training. Interestingly, our hyper-network can be regarded as a 'probe' revealing which components need to be changed to manipulate factual knowledge; our analysis shows that the updates tend to be concentrated on a small subset of components. Source code available at https://github.com/nicola-decao/KnowledgeEditor

Insights on "Editing Factual Knowledge in LLMs"

The paper "Editing Factual Knowledge in LLMs" by Nicola De Cao, Wilker Aziz, and Ivan Titov introduces KnowledgeEditor, a proposed solution for modifying factual knowledge embedded in LLMs (LMs) without resorting to extensive re-training. This paper addresses the necessity of adjusting LMs' predictions to align with dynamically changing facts, while minimizing disruption to existing knowledge. This is crucial in tasks like fact-checking and question answering, where accuracy and consistency of information are paramount.

Problem Definition and Solution

The central problem tackled by this paper is the implicit embedding of factual knowledge within the parameters of LMs, which are not easily altered. Traditional methods involve resource-intensive re-training or fine-tuning. This work introduces KnowledgeEditor, a hyper-network that applies constrained optimization techniques to selectively update LM parameters. The process is designed to modify specific factual predictions without compromising the integrity of the overall knowledge base within the model.

Methodology and Results

A crucial aspect of KnowledgeEditor is its ability to act without the necessity of specialized pre-training setups, such as meta-learning, providing flexibility and generality. The robustness of the solution is demonstrated on two popular LM architectures: BERT for fact-checking and BART for question answering. KnowledgeEditor is shown to successfully alter predictions for target inputs, ensuring consistency across paraphrase variations and maintaining reliability by affecting a minimal subset of model components.

The quantitative results underline several key claims:

• KnowledgeEditor achieves high success rates in changing targeted predictions while maintaining an equivalence accuracy across paraphrased inputs.
• Retain accuracy is well-preserved, ensuring that the rest of the model's predictions remain stable when modifying specific knowledge points, marking a substantial advantage over traditional fine-tuning approaches.
• The paper also showcases that the hyper-network can act as a `probe' for locating and updating critical neural components linked to a given fact, suggesting a possible avenue for causal exploration within model architectures.

Implications and Future Directions

Practically, KnowledgeEditor offers a computationally efficient method for rapidly updating LMs as new information becomes available, which is particularly vital for models deployed in real-time systems dependent on factual accuracy. Theoretically, this technique introduces a potential shift in how dynamic updates to static-provided knowledge bases in LMs can be handled.

Moving forward, such editability might become a staple feature for neural models, especially those used in environments with constantly evolving knowledge bases, like legal, medical, and geopolitical information systems. Furthermore, leveraging this capability in managing misinformation by correcting models to disallow propagation of outdated facts is another intriguing avenue.

Future work can explore the underlying encoding of specific knowledge types within LMs, offering insights into the architecture's knowledge representation. By integrating more refined techniques to assess and monitor the impact of parameter alterations, the field can move towards more transparent and interpretable model behaviors.

Overall, the proposal offers a significant step towards adaptive LLMs, optimizing both their operational efficiency and factual reliability without engaging in exhaustive resource commitments.