Identifying Crucial Neurons for Knowledge Storage in Large Language Models

核心概念

Identifying the most important neurons that directly contribute to the final predictions in large language models is essential for understanding their knowledge storage mechanisms.

摘要

The paper proposes a static method for pinpointing significant neurons in large language models (LLMs) that are crucial for storing and retrieving factual knowledge. The method is based on analyzing the distribution change caused by each neuron and discovering that both the neuron's coefficient score and the final prediction's ranking when projecting the neuron's subvalue into the vocabulary space play significant roles.

The key highlights and insights from the paper are:

1. Both attention and feed-forward network (FFN) layers in LLMs can store knowledge, and the most important neurons directly contributing to knowledge prediction are in the deep layers.

2. In attention layers, knowledge with similar semantics (e.g., language, country, city) tends to be stored in the same attention heads, while knowledge with distinct semantics (e.g., country, color) is stored in different heads.

3. While numerous neurons contribute to the final prediction, intervening on a few value neurons (300) or query neurons (1,000) can significantly influence the final prediction.

4. FFN value neurons are mainly activated by medium-deep attention value neurons, while these attention neurons are mainly activated by shallow/medium FFN query neurons.

The proposed method outperforms seven other static attribution methods across three evaluation metrics, demonstrating its effectiveness in identifying the crucial neurons for knowledge storage in LLMs. The analysis provides valuable insights into the mechanisms of knowledge storage and sets the stage for future research in knowledge editing.

统计

"Intervening the top200 attention neurons and top100 FFN neurons for each sentence, the MRR and probability decreases 96.3%/99.2% in GPT2, and 96.9%/99.6% in Llama."
"Randomly intervening 1,000 neurons only result in a decrease of 0.8%/1.1%."

引用

"Both attention and FFN layers can store knowledge, and all important neurons directly contribute to knowledge prediction are in deep layers."
"While numerous neurons contribute to the final prediction, intervening on a few value neurons (300) or query neurons (1,000) can significantly influence the final prediction."
"FFN value neurons are mainly activated by medium-deep attention value neurons, while these attention neurons are mainly activated by shallow/medium FFN query neurons."

从中提取的关键见解

Neuron-Level Knowledge Attribution in Large Language Models

by Zeping Yu, S... 在 arxiv.org 09-26-2024

https://arxiv.org/pdf/2312.12141.pdf

更深入的查询

How can the proposed methods be extended to identify crucial neurons for other types of knowledge beyond the six explored in the paper?

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.

What are the potential risks and ethical considerations in using the proposed methods to edit the knowledge stored in large language models?

The potential risks and ethical considerations in using the proposed methods to edit the knowledge stored in large language models (LLMs) are multifaceted and warrant careful examination:

Manipulation of Model Outputs: One of the primary risks is the potential for malicious actors to exploit the methods to manipulate the model's outputs. For instance, by identifying and altering neurons associated with toxic or biased content, individuals could intentionally increase the likelihood of generating harmful or misleading information.

Loss of Model Integrity: Editing specific neurons may inadvertently compromise the integrity of the model's knowledge base. If crucial neurons are altered or removed, the model's ability to generate accurate and contextually relevant responses could be diminished, leading to a decline in performance across various tasks.

Ethical Implications of Knowledge Editing: The ability to edit knowledge raises ethical questions about accountability and responsibility. Who is responsible for the consequences of edited outputs? There is a risk that individuals or organizations may use these methods to propagate misinformation or reinforce harmful stereotypes, thereby exacerbating societal issues.

Transparency and Explainability: The methods proposed for neuron-level knowledge attribution may lack transparency, making it difficult to understand the implications of editing specific neurons. Ensuring that the processes involved in knowledge editing are explainable is crucial for fostering trust in LLMs and their applications.

Regulatory and Compliance Issues: As LLMs become increasingly integrated into various sectors, including healthcare, finance, and education, the potential for misuse necessitates regulatory oversight. Establishing guidelines for the ethical use of knowledge editing methods is essential to mitigate risks and ensure compliance with legal and ethical standards.

Impact on User Trust: If users become aware that LLMs can be manipulated to produce biased or harmful outputs, their trust in these systems may erode. Maintaining user trust is vital for the continued adoption and acceptance of AI technologies.

In summary, while the proposed methods for editing knowledge in LLMs offer exciting possibilities for improving model performance, they also present significant risks and ethical challenges that must be addressed through careful consideration and responsible practices.

How do the findings on the importance of query neurons in activating value neurons relate to the broader understanding of information processing in the human brain?

The findings on the importance of query neurons in activating value neurons in large language models (LLMs) draw intriguing parallels to the broader understanding of information processing in the human brain:

Hierarchical Information Processing: Just as the human brain processes information hierarchically, with different regions specializing in various functions, the architecture of LLMs reflects a similar structure. Query neurons can be seen as analogous to cognitive processes that retrieve relevant information from memory (value neurons) based on contextual cues, mirroring how the brain activates specific neural pathways to access stored knowledge.

Role of Attention Mechanisms: The attention mechanisms in LLMs, which allow query neurons to focus on relevant value neurons, resemble the brain's attentional processes. In cognitive neuroscience, attention is known to enhance the processing of pertinent information while suppressing distractions, facilitating efficient information retrieval. This similarity underscores the importance of attention in both artificial and biological systems.

Neural Activation Patterns: The activation of query neurons leading to the activation of value neurons parallels the concept of neural activation patterns in the brain. When certain stimuli are presented, specific neural circuits are activated, leading to the retrieval of associated memories or knowledge. This relationship highlights the interconnectedness of neurons in both LLMs and the human brain, where the activation of one set of neurons can influence the activity of another.

Memory Retrieval and Contextual Relevance: The findings suggest that query neurons play a crucial role in determining which value neurons are activated based on the context of the input. This mirrors how the human brain retrieves memories based on contextual cues, emphasizing the importance of context in both artificial and biological information processing.

Implications for Cognitive Models: Understanding the dynamics between query and value neurons in LLMs can inform cognitive models of human memory and learning. Insights gained from studying these interactions in artificial systems may lead to a deeper understanding of how the brain organizes and retrieves knowledge, potentially influencing fields such as cognitive psychology and neuroscience.

In conclusion, the findings on query and value neurons in LLMs not only enhance our understanding of artificial intelligence but also provide valuable insights into the mechanisms of information processing in the human brain, highlighting the shared principles underlying both systems.