Retrieval-Enhanced Knowledge Editing for Improving Multi-Hop Question Answering in Language Models

Core Concepts

The core message of this article is that the authors propose a novel Retrieval-Augmented Editing (RAE) framework to effectively handle multi-hop questions in language model editing. RAE first retrieves the most relevant edited facts using a mutual information-based retrieval strategy, and then refines the language model through in-context learning with the retrieved facts.

Abstract

The article discusses the challenge of updating language models' knowledge in real-time, which leads to the risk of generating outdated or incorrect responses, especially for multi-hop questions that require integrating multiple pieces of knowledge. To address this issue, the authors introduce the Retrieval-Augmented Editing (RAE) framework. Key highlights: The authors construct an external knowledge graph that combines edited and unedited facts to enable effective retrieval of relevant knowledge for multi-hop questions. The proposed retrieval strategy is based on maximizing the mutual information between the retrieved subgraph and the target question, leveraging the reasoning capabilities of language models. A pruning technique is introduced to eliminate redundant information from the retrieved facts, mitigating the hallucination problem in language model editing. Theoretical justification is provided for the effectiveness of the proposed retrieval objective in triggering in-context learning for model editing. Comprehensive experiments across various language models validate the superiority of the RAE framework compared to state-of-the-art baselines.

Stats

The authors evaluate their method on the MQUAKE-CF and MQUAKE-T datasets, which contain 1000 counterfactual editing instances per 2-hop, 3-hop, and 4-hop questions in MQUAKE-CF, and a total of 1868 editing instances for 2-hop and 3-hop questions in MQUAKE-T. The authors report the edited accuracy, which measures the success rate of the edited language models in correctly predicting the updated answers within the first ten generated tokens.

Quotes

"Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge updates, leading to potentially outdated or inaccurate responses." "Answering multi-hop questions requires the integration of multiple pieces of knowledge." "Successful model editing for multi-hop questions requires that the edited LLMs identify and adopt the updated knowledge to derive the final answers."

Key Insights Distilled From

Retrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models

by Yucheng Shi,... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19631.pdf

Deeper Inquiries

How can the proposed retrieval-augmented editing framework be extended to handle more complex reasoning tasks beyond multi-hop question answering?

The proposed retrieval-augmented editing framework can be extended to handle more complex reasoning tasks by incorporating additional layers of abstraction and context. One way to achieve this is by integrating external knowledge sources or domain-specific databases to enrich the retrieved facts. By expanding the scope of information available for retrieval, the model can access a wider range of data to support more intricate reasoning tasks. Additionally, implementing advanced natural language processing techniques such as semantic parsing and entity linking can enhance the model's ability to understand and process complex queries. Furthermore, incorporating reinforcement learning mechanisms to guide the retrieval process based on feedback from previous interactions can improve the model's decision-making capabilities in complex scenarios.

What are the potential limitations of the current mutual information-based retrieval strategy, and how can it be further improved to handle a wider range of knowledge editing scenarios?

One potential limitation of the current mutual information-based retrieval strategy is its reliance on pre-defined thresholds or parameters, which may not always capture the nuanced relationships between facts in a knowledge graph. To address this limitation, the strategy can be further improved by implementing adaptive learning mechanisms that dynamically adjust the retrieval criteria based on the specific characteristics of the input query. Additionally, incorporating contextual embeddings and attention mechanisms can enhance the model's ability to capture subtle semantic relationships between facts, enabling more accurate retrieval of relevant information. Furthermore, integrating domain-specific knowledge graphs and ontologies can provide additional context for the retrieval process, improving the model's performance in handling a wider range of knowledge editing scenarios.

Given the importance of reducing redundant information in the retrieved facts, are there other techniques beyond the proposed uncertainty-based pruning that could be explored to enhance the editing performance?

In addition to uncertainty-based pruning, other techniques that could be explored to enhance editing performance include: Semantic Similarity Analysis: Utilizing advanced semantic similarity algorithms to identify and eliminate redundant facts that do not contribute significantly to the model's understanding of the query. By comparing the semantic relevance of retrieved facts, the model can prioritize the most informative data for editing. Graph-based Filtering: Implementing graph-based algorithms to analyze the relationships between facts in the knowledge graph and filter out redundant or irrelevant information. By considering the connectivity and importance of each fact within the graph, the model can optimize the selection of facts for editing. Contextual Relevance Scoring: Introducing a scoring mechanism that evaluates the contextual relevance of each fact to the input query. By assigning weights based on the relevance of each fact to the query context, the model can focus on retrieving and utilizing the most contextually relevant information for editing. Temporal Knowledge Filtering: Incorporating temporal knowledge filtering techniques to prioritize recent and up-to-date information in the retrieval process. By considering the temporal relevance of facts, the model can ensure that the edited knowledge reflects the most current and accurate data available.