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Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
April 16, 2024, 4:51 a.m. | Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui
cs.CL updates on arXiv.org arxiv.org
Abstract: Transformers are ubiquitous in wide tasks. Interpreting their internals is a pivotal goal. Nevertheless, their particular components, feed-forward (FF) blocks, have typically been less analyzed despite their substantial parameter amounts. We analyze the input contextualization effects of FF blocks by rendering them in the attention maps as a human-friendly visualization scheme. Our experiments with both masked- and causal-language models reveal that FF networks modify the input contextualization to emphasize specific types of linguistic compositions. In …
abstract analyze arxiv attention components contextualization cs.cl effects lens maps pivotal rendering tasks them through transformers type
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