Summary of On Affine Homotopy Between Language Encoders, by Robin Sm Chan et al.
On Affine Homotopy between Language Encoders
by Robin SM Chan, Reda Boumasmoud, Anej Svete, Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel, Mrinmaya Sachan, Bernhard Schölkopf, Mennatallah El-Assady, Ryan Cotterell
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper investigates the concept of similarity between pre-trained language encoders, which are crucial components in many NLP tasks. The researchers propose a novel approach to measure intrinsic similarity that is independent of specific tasks and still informative of extrinsic similarity, i.e., performance on downstream tasks. They introduce the notion of affine alignment, which is an asymmetric but informative metric for measuring extrinsic similarity. Experimental results on datasets of natural language representations confirm the effectiveness of this approach in bounding extrinsic similarity and revealing the structure of the pre-trained encoder space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how similar two language encoders are, even if they’re not exactly the same. It’s like trying to understand what makes different versions of a map similar, without looking at the actual locations on the map. The researchers found that there is an “affine alignment” way of measuring similarity that works well and helps us figure out how good these encoders are at doing specific tasks. This can even help us understand the overall structure of how all these encoders relate to each other. |
Keywords
» Artificial intelligence » Alignment » Encoder » Nlp
