The proposed methods for neuron-level knowledge attribution can be extended to identify crucial neurons for other types of knowledge by adapting the framework to accommodate various knowledge categories. This can be achieved through the following steps:
Dataset Expansion: To explore additional knowledge types, researchers can curate datasets that encompass a broader range of knowledge categories, such as historical events, scientific concepts, or cultural references. By extracting query-answer pairs relevant to these new categories, the model can be tested on its ability to retrieve and utilize this information.
Refinement of Importance Metrics: The log probability increase method can be fine-tuned to account for the specific characteristics of the new knowledge types. For instance, different types of knowledge may require distinct importance scoring mechanisms that reflect their unique contributions to the model's predictions.
Layer and Neuron Analysis: The analysis of neuron importance can be expanded to include a more granular examination of different layers and heads within the transformer architecture. By investigating how various layers contribute to the storage and retrieval of new knowledge types, researchers can identify which neurons are most critical for these processes.
Cross-Model Validation: Applying the methods across different large language models (LLMs) can provide insights into the generalizability of the findings. By comparing how different models store and retrieve various types of knowledge, researchers can identify common patterns and unique characteristics.
Incorporation of External Knowledge Sources: Integrating external knowledge bases or ontologies can enhance the model's ability to understand and attribute knowledge. By aligning the model's internal representations with established knowledge frameworks, researchers can better identify crucial neurons associated with new knowledge types.
By following these steps, the proposed methods can be effectively adapted to uncover crucial neurons for a wider array of knowledge categories, thereby enriching our understanding of knowledge storage in LLMs.