Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models

By design, large language models (LLMs) are static general-purpose models, expensive to retrain or update frequently. As they are increasingly adopted for knowledge-intensive tasks, it becomes evident that these design choices lead to failures to generate factual, relevant, and up-to-date knowledge. To this end, we propose Knowledge Card, a modular framework to plug in new factual and relevant knowledge into general-purpose LLMs. We first introduce knowledge cards -- specialized language models trained on corpora from specific domains and sources. Knowledge cards serve as parametric repositories that are selected at inference time to generate background knowledge for the base LLM. We then propose three content selectors to dynamically select and retain information in documents generated by knowledge cards, specifically controlling for relevance, brevity, and factuality of outputs. Finally, we propose two complementary integration approaches to augment the base LLM with the (relevant, factual) knowledge curated from the specialized LMs. Through extensive experiments, we demonstrate that Knowledge Card achieves state-of-the-art performance on six benchmark datasets. Ultimately, Knowledge Card framework enables dynamic synthesis and updates of knowledge from diverse domains. Its modularity will ensure that relevant knowledge can be continuously updated through the collective efforts of the research community.

Summary Notes

Enhancing Language Models with Knowledge Cards

Key Components of the Knowledge Card Framework

• Knowledge Cards: These are specialized knowledge bases for different domains, providing targeted information.

• Content Selectors: Tools that ensure the information from knowledge cards is relevant, concise, and accurate.

• Integration Approaches: Methods for incorporating the knowledge from cards into LLMs seamlessly.

The Importance of Knowledge Cards

• Training on diverse data across many domains.

• Acting as modular units that are used based on the specific query.

• Giving LLMs access to up-to-date and accurate information.

Ensuring Quality with Content Selectors

• Relevance Selector: Ensures the information is relevant to the query.

• Pruning Selector: Trims content to manageable sizes for LLM integration.

• Factuality Selector: Checks the accuracy of the information provided.

Integrating Knowledge into LLMs

• Bottom-Up Approach: Starts with activating relevant knowledge cards, refining their output, and then integrating it into the LLM.

• Top-Down Approach: The LLM identifies when it needs external knowledge and activates the appropriate knowledge cards.

Demonstrated Success

Challenges and Ethical Considerations

• Risks of generating low-quality knowledge.

• Potential biases towards certain domains.

• The need for LLMs to better identify when external knowledge is needed.

Future Directions

• Improving knowledge generation and accuracy.

• Enhancing LLMs' ability to seek external knowledge.

• Expanding the range of knowledge sources.

• Addressing ethical concerns and ensuring responsible use.

Conclusion

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