Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications

Introduction

LLMs like GPT-3 have made significant strides in their ability to encode real-world knowledge within their parameters. They have shown prowess in a variety of natural language processing tasks. However, there are lingering challenges, particularly knowledge-intensive tasks that necessitate not just vast amounts of world knowledge, but also current and domain-specific knowledge. LLMs often struggle with data that isn't adequately represented in their training sets, including long-tail knowledge and information that changes over time. This has sparked interest in methods for integrating up-to-date external knowledge into LLMs. In this context, the paper presents a comprehensive survey that examines trends and strategies in this space, specifically focusing on knowledge editing and retrieval augmentation methods.

Knowledge Editing

Knowledge editing techniques are identified as a key method for addressing the outdated or erroneous information instilled within LLM parameters. The paper discusses various methods of knowledge editing, which includes manipulating the input, model parameters, or assessing the output for knowledge modification. This is classified into three distinct categories:

1. Input Editing: A straightforward option that involves prompt manipulation to enhance or refine the information prior to model processing.
2. Model Editing: Entails more granular changes wherein the model parameters directly linked to the outdated knowledge are located and updated.
3. Assess Knowledge Editing: Involves evaluating the extent of knowledge modification post-editing and fine-tuning by scrutinizing the output using specified benchmarks.

The taxonomy provided traces how editing tasks can enhance LLMs' capabilities without fundamentally altering the architecture or core learned parameters.

Retrieval Augmentation

The paper elaborates on retrieval augmentation as an alternative to knowledge editing that maintains LLM parameters unchanged. The core underlying idea is to fetch relevant documents from an external corpus on-the-fly during inference. Here are key components tackled in this strategy:

1. Retrieval Judgement: Determining the appropriate scenarios when an LLM should seek external information.
2. Document Retrieval: Implementing a retrieval system that can range from traditional search engines to generative techniques that produce documents aligned with the input query.
3. Document Utilization: Utilizing the retrieved documents at various stages of prediction, including pre-prompt augmentation, mid-reasoning verification, and post-answer revision.
4. Knowledge Conflict: Addressing discrepancies between parameter-stored knowledge and retrieved documents or among the retrieved documents themselves.

The paper promotes further exploration into dealing with conflicting knowledge sources, highlighting a potential research direction.

Frontier Applications

LLMs with incorporated knowledge editing and retrieval mechanisms show remarkable potential for real-world applications. The paper briefly discusses pioneering applications like LangChain and ChatDoctor, which leverage such enhancements to provide relevant, up-to-date responses within their specialized domains. Furthermore, tools like New Bing integrate retrieval augmentation directly into the search process, enhancing both the quality and relevancy of generated content.

Conclusion

The survey acknowledges that while knowledge editing and retrieval augmentation have made progress, there remains substantial room for advancement. Future research opportunities are abundant, from developing methods that can incorporate multi-source and multi-format knowledge to refining techniques that will enable LLMs to solve more complex tasks with an even higher degree of reliability and domain-specificity. The survey serves as a compass for the ongoing and future endeavors in this exciting and rapidly evolving research area.