Neuron-Level Knowledge Attribution in Large Language Models
This paper explores how to identify which parts of large language models are most important for making predictions. It introduces a new method that is more efficient than existing ones and helps in understanding how knowledge is stored in these models.
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- 1 In this section, we aim to locate important neurons for specific predictions.
- 2 It is crucial to quantitatively compare the importance of both attention and FFN layers.
- 3 In order to analyze which components of v is important, we design several bs(x) and bs(v) and compute the distribution change.
- 4 This is similar to direct logit attribution (DLA) in Wang et al. . c) probability increase: p(w|mv l + A l + h l-1 )p(w|A l + h l-1 ) d) norm:.
Introduction
Transformer-based large language models (LLMs) possess remarkable capabilities for storing factual knowledge, which is important for downstream tasks including question answering and reasoning . Firstly, existing studies often depend on causal tracing and integrated gradients for knowledge attribution.
However, many studies point out that the computational complexity of forward and backward operations in these methods restricts their applicability to millions of neurons in LLMs, which are proved as fundamental units for storing knowledge .
Secondly, while a few studies have devised methods for analyzing neurons, they often lack comparisons with other methods.
The first limitation of our study is that it focuses on six specific types of knowledge, while other types of knowledge are also important.
We plan to explore these areas in future work.
Research Question
In this section, we aim to locate important neurons for specific predictions.
Methodology
Compared with seven other static methods, our proposed method achieves the best performance on three metrics. Furthermore, since the identified neurons directly contribute to the final predictions’ probability, we also develop a static method to identify “query neurons” that aid in activating these “value neurons”.
Study Design
Overall, our contributions are as follows: a) We design a static method for neuron-level knowledge attribution in large language models.
Compared with seven static methods, our method achieves the best performance under three metrics.
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Results & Findings
While recent studies have made significant progress in understanding knowledge localization and the information flow from inputs to predictions, it is still hard to identify exact parameters for knowledge storage in LLMs due to several reasons. It is crucial to quantitatively compare the importance of both attention and FFN layers.
- While recent studies have made significant progress in understanding knowledge localization and the information flow from inputs to predictions, it is still hard to identify exact.
- It is crucial to quantitatively compare the importance of both attention and FFN layers.
- We analyze the distribution change caused by each neuron and discover that both the neuron’s coefficient score and the final prediction’s ranking, when projecting this neuron’s.
- Based on this finding, we employ log probability increase as importance score, enabling the identification of neurons that contribute significantly to final predictions.
- Specifically, we calculate the inner products between the query neurons and value neurons as importance scores.
It is crucial to quantitatively compare the importance of both attention and FFN layers.
In order to analyze which components of v is important, we design several bs(x) and bs(v) and compute the distribution change.
Practical Applications
However, in addition to these “value neurons”, there exist “query neurons” that aid in activating these neurons, even if they may not directly contain information about w. There are other tokens competing with the correct knowledge token, so the neurons with large coefficient scores may be related to these tokens.
Nevertheless, reproducing the importance of the deepest layers may be a prospective avenue for developing improved attribution methods.
Hence, to explore the interpretability of query neurons may be a valuable direction in future works. neuron top10 tokens in vocabulary space.
Mechanistic Interpretability
This section focuses on mechanistic interpretability, which aims to reverse engineer the circuits from inputs to predictions. It discusses the importance of projecting internal vectors into vocabulary space and the role of attention heads.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1 :: Figure 1: (a) Query neurons in shallow FFN layers. (b) Attention query/value neurons in attention heads. (c) Value neurons in deep FFN layers.
- Figure 2 :: Figure 2: Neuron distribution on all layers in Llama-7B.
- Figure 3 :: Figure 3: Curves of log probability increase (left) and probability increase (right) on Llama-7B.
- Figure 4 :: Figure 4: Top10 important “value layers” in GPT2.
- Figure 5 :: Figure 5: Top10 important “value layers” in Llama.
Frequently Asked Questions
However, many studies point out that the computational complexity of forward and backward operations in these methods restricts their applicability to millions of neurons in LLMs, which are proved as fundamental units for storing knowledge . In this section, we aim to.
For each sentence, we apply every method to identify top10 FFN neurons, and evaluate the attributed neurons using three metrics. Overall, our analysis learns the information flow at neuron level: features in shallow/medium FFN neurons are extracted, then activate the deep attention.
It is crucial to quantitatively compare the importance of both attention and FFN layers. In order to analyze which components of v is important, we design several bs(x) and bs(v) and compute the distribution change.
Therefore, if a “query” neuron/subvector exhibits a larger inner product with the subkey compared to another one, it is more helpful for activating the “value neuron”. There are other tokens competing with the correct knowledge token, so the neurons with large coefficient.
The first limitation of our study is that it focuses on six specific types of knowledge, while other types of knowledge are also important. We plan to explore these areas in future work.
This paper explores how to identify which parts of large language models are most important for making predictions. It introduces a new method that is more efficient than existing ones and helps in understanding how knowledge is stored in these models.