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

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

The field of Natural Language Processing (NLP) is evolving rapidly, with Large Language Models (LLMs) playing a pivotal role.
However, LLMs often struggle to provide current, accurate, and domain-specific information. The Knowledge Card framework aims to fill these gaps, enhancing LLMs' capabilities significantly.

Key Components of the Knowledge Card Framework

The framework is built on three main components:
  • 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

Knowledge cards are crucial for:
  • 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

Content selectors play a vital role in maintaining the quality of information:
  • 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

Two main approaches are used for integration:
  • 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

Experiments have shown the framework's effectiveness, with notable improvements in LLM performance across various tasks and benchmarks. It also allows for the dynamic updating of LLMs with new knowledge.

Challenges and Ethical Considerations

Despite its potential, the framework faces challenges such as:
  • 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

Future developments focus on:
  • 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

The Knowledge Card framework offers a promising approach to augmenting LLMs with specialized knowledge, addressing key limitations in current NLP technologies.
Ongoing research and development will be crucial for overcoming challenges and maximizing the framework's potential.

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