Uncovering Model Processing Strategies with Non-Negative Per-Example Fisher Factorization

arXiv:2310.04649v3 Announce Type: replace Abstract: We introduce NPEFF (Non-Negative Per-Example Fisher Factorization), an interpretability method that aims to uncover strategies used by a model to generate its predictions. NPEFF decomposes per-example Fisher matrices using a novel decomposition algorithm that learns a set of components represented by learned rank-1 positive semi-definite matrices. Through a combination of human evaluation and automated analysis, we demonstrate that these NPEFF components correspond to model processing strategies for a variety of language models and text proce

Source: arXiv cs.LG — read the full report at the original publisher.

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