Unlocking the Mysteries of Large Language Models: Neuron Universality Unveiled

Understanding of Large Language Models (LLMs) Decision-making Process

As Large Language Models (LLMs) have become prominent in high-stakes applications, it’s important to understand their decision-making processes. This is crucial to help mitigate potential risks. Artificial neural networks are observable and deterministic, fueling research into their interpretability. This not only enhances knowledge but also helps develop AI systems that minimize harm.

Universality of Individual Neurons in GPT2 Language Models

Research has explored the universality of individual neurons in GPT2 language models. This aims to identify and analyze neurons exhibiting universality across models with distinct initializations. The extent of universality in these neurons has profound implications for developing methods to understand and monitor neural circuits.

Study Focus and Findings

The study focuses on transformer-based auto-regressive language models, particularly the GPT2 series and experiments with the Pythia family. The findings challenge the notion of universality across the majority of neurons, as only a small percentage (1-5%) passes the threshold for universality.

Features of Universal Neurons

The research delves into the statistical properties of universal neurons. These neurons stand out from non-universal ones, exhibiting distinctive characteristics in weights and activations. The findings categorize these neurons into families such as unigram, alphabet, previous token, position, syntax, and semantic neurons.

Moving Forward

While only a fraction of neurons exhibit universality, future research can focus on larger models, exploring their response to stimulus or perturbation, development over training, and impact on downstream components.

For more details on the research, check out the paper and GitHub repository. This research provides valuable insights into the development of Large Language Models (LLMs).

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