Limitations of the Knowledge Neuron Thesis in Explaining Language Model Capabilities

แนวคิดหลัก

The Knowledge Neuron (KN) thesis, which proposes that facts are stored in the MLP weights of language models, is an oversimplification. Language models exhibit complex mechanisms for processing both linguistic and factual information that cannot be fully explained by the KN thesis.

บทคัดย่อ

The paper reassesses the Knowledge Neuron (KN) thesis, which suggests that facts are recalled from a language model's training corpus through the MLP weights in a manner resembling key-value memory. The authors find that this thesis is an oversimplification and does not adequately explain the process of factual expression in language models.
The authors first evaluate the KN thesis by localizing syntactic phenomena, such as determiner-noun agreement, to individual neurons using the same methods as for factual information. They find that the characteristics of localization are similar for both linguistic and factual information, suggesting a unified underlying mechanism.
However, the authors also find that editing the identified "knowledge neurons" is not enough to overturn the final model predictions, indicating that the patterns stored in these neurons do not constitute true "knowledge." The patterns appear to be more akin to complex token expression patterns that can be interpreted linguistically, but do not fit into well-defined linguistic or factual categories.
The authors then re-evaluate Meng et al.'s (2022) Rank-One Model Editing (ROME) framework, which proposes a more intricate two-step process for factual recall. The authors find that ROME's editing only superficially alters token association patterns, and fails to generalize under new criteria of bijective symmetry and synonymous invariance.
The authors conclude that the feed-forward MLP modules of transformer models do not store knowledge, but rather complex "token expression patterns." To gain a more comprehensive understanding of language model capabilities, the authors argue that we need to look beyond the MLP weights and explore the rich layer and attention structures of recent models.

สถิติ

Editing the identified "knowledge neurons" only leads to a 5.2% change in the categorical predictions made by the language model.
The reliability scores of the KN edit method range from 1.66% to 47.86%, which is not enough to support the KN thesis.
The ROME model editing method achieves higher reliability scores than KN edit, but performs poorly under the new criteria of bijective symmetry (23.71-33.64%) and synonymous invariance (52.35-58.36%).

คำพูด

"Not only have we found that we can edit the expression of certain linguistic phenomena using the same model editing methods but, through a more comprehensive evaluation, we have found that the KN thesis does not adequately explain the process of factual expression."
"Whatever is being manipulated reflects none of the traditional tautologies that have been associated with "knowledge," as that term has been understood in philosophy since the time of Aristotle."
"We therefore argue for the position that the feed-forward MLP modules of the transformer model do not store knowledge, but rather complex "token expression patterns.""

ข้อมูลเชิงลึกที่สำคัญจาก

What does the Knowledge Neuron Thesis Have to do with Knowledge?

by Jingcheng Ni... ที่ arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02421.pdf

สอบถามเพิ่มเติม

How can we better characterize the underlying mechanisms that enable language models to process both linguistic and factual information?

To better characterize the underlying mechanisms that enable language models to process both linguistic and factual information, we need to move beyond oversimplified frameworks like the Knowledge Neuron (KN) thesis. While the KN thesis suggests that language models recall facts through multi-layer perceptron (MLP) weights resembling key-value memory, it falls short in explaining the complex processes involved.
One approach could involve delving deeper into the intricate layer structures and attention mechanisms of language models. By exploring how information flows through different layers and how attention is allocated to different parts of the input, we can gain a more comprehensive understanding of how language models process linguistic and factual information. This would involve analyzing not just the MLP weights but also the interactions between different components of the model.
Additionally, incorporating insights from causal tracing methods can help uncover the causal relationships between different parts of the model and the final output. By tracing how changes in input tokens affect the model's predictions, we can gain a more nuanced understanding of how linguistic and factual information is processed and represented within the model.

What alternative frameworks or models could provide a more comprehensive explanation of language model capabilities beyond the limitations of the Knowledge Neuron thesis?

One alternative framework that could provide a more comprehensive explanation of language model capabilities is a circuit interpretation approach. This approach focuses on understanding the entire decision-making circuit within the model, rather than just the MLP weights. By examining how information flows through different components of the model, such as the MLP, attention mechanisms, and other modules, we can gain insights into how linguistic and factual information is processed and represented.
Another model that could offer a more holistic explanation is one that integrates symbolic reasoning with neural network processing. By combining the strengths of symbolic AI, which excels at representing explicit knowledge and rules, with the power of neural networks in capturing complex patterns, we can create models that are more interpretable and controllable. This hybrid approach could provide a more nuanced understanding of how language models operate and enable better control over their behavior.

Given the limitations of the KN thesis, how might we need to rethink the way we approach the interpretability and controllability of language models?

In light of the limitations of the KN thesis, we may need to rethink our approach to interpretability and controllability of language models. Instead of focusing solely on editing MLP weights or identifying key neurons, we should consider a more holistic view of the model's decision-making process. This could involve analyzing the interactions between different components of the model, such as attention mechanisms, positional encodings, and transformer blocks.
Furthermore, we should explore methods that go beyond superficial cues like word co-occurrence frequencies. By looking at how different parts of the model contribute to the final output and how they interact with each other, we can gain a deeper understanding of the model's behavior. This could involve developing new evaluation criteria that assess the model's performance on a wider range of tasks and phenomena, including syntactic and semantic patterns.
Overall, rethinking our approach to interpretability and controllability of language models requires a shift towards more comprehensive and nuanced analyses that consider the model as a complex system of interconnected components rather than isolated parts. This holistic perspective can lead to more effective strategies for understanding and managing the behavior of language models.