Uncovering the Internal Mechanisms of Large Language Models through Human Behavioral Patterns

Core Concepts

Large Language Models exhibit similar word prediction patterns to humans, with increased attention on crucial words leading to more confident predictions, in contrast to Shallow Language Models.

Abstract

The paper investigates the internal mechanisms of Large Language Models (LLMs), such as GPT-2, by correlating their values with human eye-tracking measurements, which are recognized as meaningful indicators of human reading patterns.
The key findings are:

Feed-Forward Networks (FFN) in the middle layers of LLMs start to align with human fixation patterns, leveraging the upper layers to broaden the contextual understanding. This suggests that the logits within FFN increasingly encapsulate word semantics suitable for predicting tokens from the vocabulary.

Multi-Head Self-Attention (MHSA) mechanisms in the middle and upper layers of LLMs exhibit stronger correlations with human fixations, indicating that LLMs focus on important contextual tokens similar to human behavior.

Compared to Shallow Language Models (SLMs), LLMs demonstrate a positive correlation between word prediction probabilities and human fixations, suggesting that LLMs, like humans, assign higher prediction probabilities to words that receive more attention.

This probing approach provides interpretable insights into the internal workings of LLMs and can inform the development of more reliable and trustworthy language models.

Stats

The FFN values in the middle layers of GPT-2 show the strongest correlation with human eye-tracking measures, such as Gaze Duration (GD), Total Reading Time (TRT), First Fixation Duration (FFD), and Go-Past Time (GPT).
The multi-head self-attention mechanisms in the middle and upper layers of GPT-2 exhibit stronger correlations with human fixations compared to the bottom layers.
Large Language Models (LLMs) like GPT-2 demonstrate a positive correlation between word prediction probabilities and human fixations, while Shallow Language Models (SLMs) show a consistent negative correlation.

Quotes

"Our findings reveal that LLMs exhibit a similar prediction pattern with humans but distinct from that of Shallow Language Models (SLMs)."
"Moreover, with the escalation of LLM layers from the middle layers, the correlation coefficients also increase in FFN and MHSA, indicating that the logits within FFN increasingly encapsulate word semantics suitable for predicting tokens from the vocabulary."
"Intriguingly, in the upper layers, we observe a decline in correlation values. We hypothesize that at this stage, the LLM begins to incorporate less critical words within sentences into its consideration, diverging from human intuition, which tends to focus on the most crucial aspects of the context and disregard less important information."

Key Insights Distilled From

Probing Large Language Models from A Human Behavioral Perspective

by Xintong Wang... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2310.05216.pdf

Deeper Inquiries

How can the insights from this study be leveraged to improve the interpretability and reliability of Large Language Models?

The insights from this study can be instrumental in enhancing the interpretability and reliability of Large Language Models (LLMs) in several ways. Firstly, by correlating values from LLMs with human eye-tracking measures, researchers can gain a deeper understanding of how these models process information and make predictions. This correlation can help identify patterns in LLM behavior that align with human reading patterns, thus making the model's decision-making process more transparent and interpretable.
Moreover, by analyzing the internal mechanisms of LLMs, such as feed-forward networks (FFN) and multi-head self-attention (MHSA), researchers can pinpoint areas where these models may exhibit biases or inaccuracies. Understanding these mechanisms can lead to the development of more robust and reliable LLMs by fine-tuning these components to better align with human cognitive processes.
Additionally, the findings from this study can inform the development of interpretability tools and techniques specifically tailored to LLMs. By leveraging human behavioral signals, such as eye-tracking data, in conjunction with advanced machine learning algorithms, researchers can create tools that provide real-time insights into how LLMs make predictions. These tools can help users, including researchers, developers, and end-users, better understand and trust the decisions made by LLMs, ultimately improving their interpretability and reliability.

How might the findings from this study inform the development of language models that better align with human cognitive processes and decision-making?

The findings from this study offer valuable insights into how Large Language Models (LLMs) can be developed to better align with human cognitive processes and decision-making. By correlating LLM values with eye-tracking measures, researchers can identify similarities and differences between LLM prediction patterns and human reading patterns. This comparison can guide the development of LLMs that mimic human cognitive processes more closely, leading to more intuitive and human-like decision-making.
Furthermore, the analysis of feed-forward networks (FFN) and multi-head self-attention (MHSA) in LLMs can shed light on how these models process information and make predictions. By understanding the internal mechanisms of LLMs, developers can optimize these components to prioritize essential information and context, similar to how humans focus on critical aspects during decision-making processes.
Additionally, by exploring the correlation between LLMs and Shallow Language Models (SLMs), researchers can identify key differences in prediction behaviors. This comparison can inform the development of LLMs that exhibit more human-like predictive patterns, emphasizing important words and enhancing the certainty of word predictions. By aligning LLMs more closely with human cognitive processes and decision-making, developers can create models that are not only more intuitive but also more effective in various natural language processing tasks.

What other human behavioral signals, such as fMRI or EEG, could be utilized to further probe the internal mechanisms of Large Language Models?

In addition to eye-tracking data, other human behavioral signals such as functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG) can be utilized to further probe the internal mechanisms of Large Language Models (LLMs).

fMRI: Functional Magnetic Resonance Imaging (fMRI) can provide insights into the neural activity associated with language processing in LLMs. By analyzing brain activity patterns while individuals interact with LLMs, researchers can identify regions of the brain that are activated during language comprehension and production. This information can help correlate neural responses with LLM predictions, offering a more comprehensive understanding of how these models interact with human cognitive processes.

EEG: Electroencephalography (EEG) measures electrical activity in the brain and can be used to study the temporal dynamics of language processing in LLMs. By recording EEG signals while individuals engage with LLMs, researchers can track cognitive processes in real-time, such as attention, memory, and decision-making. Analyzing EEG data alongside LLM predictions can reveal the neural signatures associated with different linguistic tasks, providing valuable insights into the internal mechanisms of these models.

By integrating fMRI and EEG data with LLM analysis, researchers can gain a multi-modal perspective on how these models interact with human cognitive processes. This holistic approach can offer a more comprehensive understanding of LLM behavior and pave the way for the development of more human-like and interpretable language models.