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Backward Lens: Projecting Language Model Gradients into the Vocabulary Space
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Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community. Recent interpretability methods project weights and hidden states obtained from the forward pass to the models' vocabularies, helping to uncover how information flows within LMs. In this work, we extend this methodology to LMs' backward pass and gradients. We first prove that a gradient matrix can be cast as a low-rank linear combination of its forward and backward passes' inputs. We then develop methods to project these gradients into vocabulary items and explore the mechanics of how new information is stored in the LMs' neurons.
Forward citations
Cited by 2 Pith papers
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Verbalizable Representations Form a Global Workspace in Language Models
Language models represent their current reasoning in a small, readable set of verbalizable vectors (the J-space) that functions like a global workspace.
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Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
Bias in GPT-2 and Llama-2 is localized to a small set of edges, and ablation of those edges reduces bias while impairing unrelated NLP tasks.
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