- The paper introduces a neuron-centric approach that maps specific neuron activations to improved arithmetic reasoning in LLMs.
- It employs GPT-4 to automate the discovery of neurons in Llama2, aligning token projections with reasoning steps in Chain-of-Thought prompts.
- The study demonstrates that targeted neuron activations are essential for performance, offering insights for advancing LLM interpretability.
An Investigation of Neuron Activation in LLMs
Introduction
This paper presents a study on understanding how Chain-of-Thought (CoT) prompts evoke arithmetic reasoning in LLMs, focusing on the activation of specific neurons within the models. The authors argue for a more unified interpretation utilizing neuron activation, particularly within Llama2, to illuminate the arithmetic reasoning process in LLMs, previously addressed mostly through empirical component ablations.
Background
Arithmetic reasoning in LLMs is often assessed through the effectiveness of CoT prompts, yet a thorough understanding of the internal processing remains elusive. Prior research has dissected CoT components to observe impacts on LLM performance, but without addressing the underlying reasons for effectiveness. Comparatively, mechanistic interpretability approaches have studied neuron functions and interactions to decipher LLM behavior, identifying neurons linked to specific human-interpretable concepts.
Methodology
The study adopts a neuron-centric approach, leveraging neuron activation as a lens to understand LLMs' reasoning mechanisms. To systematically identify neurons associated with reasoning capabilities, the authors use GPT-4 to automate the discovery of neurons within Llama2 that express concepts related to arithmetic operations and logical connectors. The methodology involves filtering for neurons with strong associations to target tokens and concepts, observed through vocabulary projections and automated concept annotation.
Results
Neuron Discovery and Importance:
The paper successfully identifies neurons corresponding to various reasoning tasks, such as addition and logical sentence progression, confirming their hypothesis of neuron specialization in arithmetic reasoning. The effectiveness of these neurons was further validated through performance drop experiments, indicating their necessity for reasoning elicitation in LLMs.
Activation Patterns:
Detailed analyses of activation patterns revealed that neuron activations align temporally with reasoning steps in CoT sequences. This provides evidence that CoT effectiveness in reasoning tasks is tied to specific neuron activations that guide the model's logical progression through tasks.
Explaining Previous Observations:
The study uses neuron activation patterns to elucidate prior empirical findings regarding CoT prompts, such as the importance of equations and textual explanations in CoT constructions. It demonstrates that equations and textual explanations substantially increase reasoning-related neuron activations, which correlates with improved model performance.
Implications and Future Work
By mapping neuron activations to reasoning tasks, the study not only enhances our understanding of LLM reasoning processes but also suggests potential pathways for future work in model interpretability and refinement. Integrating neuron activation analysis with other interpretability tools could spearhead developments in fine-tuning LLMs for more reliable and controllable reasoning tasks.
Conclusion
This investigation into neuron activation and its role in arithmetic reasoning in LLMs offers a crucial perspective complementing surface-level prompt engineering. Such neuron-centric analyses could pave the way for developing more nuanced LLMs with improved interpretability and targeted cognitive capabilities. The work underscores the necessity of advancing beyond component-based evaluations, advocating for deeper mechanistic dissections of LLM functionalities.
