Summary of Hitting “probe”rty with Non-linearity, and More, by Avik Pal et al.

Hitting “Probe”rty with Non-Linearity, and More

by Avik Pal, Madhura Pawar

First submitted to arxiv on: 25 Feb 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper proposes a novel approach to analyzing the internal representations of language models, specifically BERT, using structural probes. The traditional linear transformation-based probes may not fully capture the structure of the encoded information. To address this limitation, the authors introduce non-linear structural probes, which are designed to be simpler yet effective. A visualization framework is also developed to qualitatively assess the connections between words in a sentence and the dependency trees predicted by BERT. The study finds that radial basis function (RBF) is an effective non-linear probe for the BERT model.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper looks at how language models like BERT store information about sentences. It uses special tools called “structural probes” to figure out what’s going on inside these models. The usual way of doing this might not be enough, so they try something new: non-linear structural probes. This helps them see how words in a sentence are connected and understand what the model is really learning. They even create a way to visualize all this information! They find that one type of probe works especially well for BERT.