Summary of Explaining Text Similarity in Transformer Models, by Alexandros Vasileiou et al.
Explaining Text Similarity in Transformer Models
by Alexandros Vasileiou, Oliver Eberle
First submitted to arxiv on: 10 May 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 This paper aims to demystify the predictions made by Transformer-based models for natural language processing (NLP) tasks, particularly in unsupervised applications. By leveraging layer-wise relevance propagation (LRP), an improved explanation method, researchers have developed BiLRP to analyze the feature interactions driving similarity in NLP models. The study investigates three use cases: grammatical interactions, multilingual semantics, and biomedical text retrieval, demonstrating the utility of these explanations in analyzing corpus-level insights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers decide what’s similar or not in natural language processing tasks. They’re using special models called Transformers to do this, but we don’t really know why they make certain choices. To fix this, scientists have developed a way to explain how these models work, by looking at the interactions between words and phrases. They applied this method to three different types of text: grammar rules, language differences, and medical research papers. This helps us see what makes these texts similar or different. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Semantics » Transformer » Unsupervised
