Interpreting Language Model Parameters

Key points

• Mechanistic interpretability aims to uncover how neural networks use their parameters to implement their impressive neural algorithms.1
• Although previous work has uncovered substantial structure in the intermediate representations that networks use, little progress has been made to understand how the parameters and nonlinearities of networks perform computations on those representations.1
• The method, adVersarial Parameter Decomposition (VPD), optimizes for decompositions of neural network parameters into simple subcomponents that preserve the network's input-output behavior even when many subcomponents are ablated, including under ablations that are adversarially selected to destroy behavior.1
• While more work remains to be done to deepen our understanding of how neural networks use their parameters to compute their behavior, our work suggests an approach to identify a small set of simple, mechanistically faithful subcomponents on which further mechanistic analysis can be based.1

Sources (1)

• [1]Interpreting Language Model Parameters
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 06:06 PM

  In this work, we present a method that brings us closer to this understanding by decomposing a language model's parameters into subcomponents that each implement only a small part of the model's learned algorithm, while simultaneously requiring only a small fraction of those subcomponents to account for the network's behavior on any input.

  Mechanistic interpretability aims to uncover how neural networks use their parameters to implement their impressive neural algorithms.

Extractive summary: sentences quoted from the sources.