Learning to Edit: Aligning LLMs with Knowledge Editing

The authors of this paper include Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Li Liangyou, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, and Weihua Wang. They are affiliated with various institutions such as the Hong Kong University of Science and Technology, Macquarie University, Huawei Noah's Ark Lab, and the Department of Statistics and Actuarial Science at the University of Hong Kong, with research interests spanning natural language processing, language models, topic models, machine translation, recommendation systems, computer vision, and other fields.

Abstract

• Knowledge editing techniques aim to efficiently modify a small portion of knowledge in large language models (LLM) without negatively impacting performance on other inputs.
• Existing methods primarily rely on post-memory-updated knowledge, hindering LLMs from effectively combining new knowledge with their inherent knowledge when answering questions.
• This paper proposes a framework named Learning to Edit (LTE) that teaches LLMs how to apply updated knowledge to input questions.
• The LTE framework consists of two stages: the alignment stage and the inference stage.
• The alignment stage fine-tunes the LLM on a meticulously crafted parallel dataset to cultivate its ability to perform reliable, relevant edits while preserving information and linguistic abilities outside the scope.
• The inference stage uses a retrieval-based mechanism for real-time and large-scale knowledge editing.

1. Introduction

• The dynamic nature of LLMs requires frequent updates to correct outdated information or integrate new knowledge.
• The concept of knowledge editing aims to efficiently modify LLM outputs to be specific to certain queries while preserving performance on other unrelated queries.
• Existing methods primarily rely on post-memory-updated knowledge, which hinders LLMs from effectively combining new knowledge with their inherent knowledge when answering questions.
• This paper proposes a framework named Learning to Edit (LTE) that aims to teach LLMs how to apply updated knowledge to input questions.

2. Task Formulation

• The goal of knowledge editing is to effectively adjust the behavior of an initial base LLM in response to specific edit descriptors.
• The effectiveness of knowledge editing is evaluated across four dimensions: edit success, portability, locality, and fluency.

3. Methodology

• This paper proposes a framework named Learning to Edit (LTE), which is composed of two stages: the alignment stage and the inference stage.
• The alignment stage fine-tunes the LLM to cultivate its ability to perform reliable, relevant edits while preserving information and linguistic abilities outside the scope.
• The inference stage uses a retrieval-based mechanism for real-time and large-scale knowledge editing.

3.1 Alignment Stage: Learning to Edit

• The alignment stage aims to cultivate three key abilities of the LLM: within-scope ability, out-of-scope ability, and linguistic ability.
• The LLM is fine-tuned on a meticulously crafted parallel dataset to cultivate these abilities.
• The parallel dataset consists of each edit descriptor paired with corresponding within-scope and out-of-scope queries.

3.2 Inference Stage: Real-time Editing

• The inference stage uses a retrieval-based mechanism for real-time and large-scale knowledge editing.
• The retrieval model embeds edit descriptors and creates vector memories to store representations.
• When given a query, the retrieval model retrieves the most relevant edit descriptor and inputs it along with the query to the LLM to obtain an answer.

4. Experiments

• The LTE was evaluated against seven advanced baseline methods on four benchmark datasets.
• LTE excels in knowledge editing performance, robustness, minimal interference with general tasks, and fast editing speed.

4.1 Experimental Setup

• LLaMA2-Chat-7B and Qwen-Chat-7B were chosen as the base models.
• The LTE method was implemented with standard fine-tuning on 60k data.
• Seven powerful knowledge editing methods were selected as baselines.

4.2 Single Edit Results

• LTE outperforms existing methods in terms of knowledge editing performance.
• LTE demonstrates excellent portability but slightly lags behind the best result in terms of locality.

4.3 Large-scale Edit Results

• LTE and LTE-LoRA excel in batch and sequential editing.
• LTE and LTE-LoRA show slower performance degradation than other methods in batch and sequential editing.

4.4 General Task Results

• LTE has minimal impact on the performance of LLMs across various domains.
• LTE can effectively perform knowledge editing while causing minimal interference with the cognitive functions of the LLM.

5. Discussion

• This paper discusses the advantages and limitations of the LTE framework.
• The LTE framework requires a one-time fine-tuning process but enables real-time knowledge editing.
• The LTE framework primarily focuses on factual knowledge editing and can be extended to other types of editing in the future.
• The LTE framework has potential in multilingual and multimodal editing.
• Applying the LTE framework to black-box models is a future research direction.

5.1 Ablation Study

• An ablation study was conducted to assess the importance of various components in the LTE framework.
• Experimental results show that both the alignment stage and the inference stage are important components of the LTE framework.

5.2 Efficiency Analysis

• The efficiency of the LTE framework was analyzed.
• The LTE framework has the fastest editing speed and outperforms other methods in terms of performance.

5.3 Case Study

• This paper demonstrates the performance of the LTE framework in a single case.
• The LTE framework can effectively apply edited knowledge to answer queries that require combined reasoning.

6. Related Work

• This paper reviews related research on knowledge editing and LLM alignment.
• The proposed method has advantages over existing methods.

7. Conclusion

• This paper proposes the Learning to Edit (LTE) framework, which can effectively perform knowledge editing in LLMs.
• The LTE framework excels in knowledge editing performance, robustness, minimal interference with general tasks, and fast editing speed.

Limitations

• The LTE framework requires a one-time fine-tuning process.
• The LTE framework primarily focuses on factual knowledge editing.
• Applying the LTE framework to black-box models is a future research direction.

Ethical Statement

• Knowledge editing needs to adhere to principles of safety and responsibility.
• The constructed data was manually evaluated to ensure its safety and integrity.

Q: What specific research methods were used in the paper?

The paper proposes a framework called Learning to Edit (LTE) for knowledge editing of large language models (LLM). The framework consists of two main phases:

• Alignment Phase: Through supervised fine-tuning on a meticulously designed parallel dataset, the LLM learns how to apply updated knowledge to input questions. This phase aims to cultivate three key capabilities in the LLM:
  • In-Scope Capability: Generate reliable and logically consistent editing results.
  • Out-of-Scope Capability: Maintain the integrity of content unrelated to the edit.
  • Linguistic Capability: Preserve linguistic fluency.
• Inference Phase: Utilize a retrieval-based mechanism to retrieve the most relevant updated knowledge from a stored memory bank and apply it in real-time to the LLM's responses, enabling batch or sequential knowledge editing.

Q: What are the main research findings and outcomes?

• LTE achieved new SOTA performance in knowledge editing tasks, improving by over 20 absolute points in terms of portability compared to existing methods.
• LTE excels in both batch and sequential editing, with a明显 lower performance degradation rate than other methods.
• LTE facilitates knowledge editing with minimal disruption, with little impact on the model's cognitive functions across various domains.
• LTE combines the fastest editing speed with superior performance.

Q: What are the current limitations of this research?

• The LTE framework requires a one-time fine-tuning process during the training phase. Although this is necessary, it lays the foundation for real-time knowledge editing.
• Current research focuses primarily on factual knowledge editing, while the scope of model editing can be expanded to include personal traits, emotional responses, opinions, and beliefs.
• Multilingual and multimodal editing are directions for future research.
• Due to the proprietary nature of leading LLMs like ChatGPT and GPT-4, the application of knowledge editing technology is limited.