Summary of Analysis and Visualization Of Linguistic Structures in Large Language Models: Neural Representations Of Verb-particle Constructions in Bert, by Hassane Kissane et al.
Analysis and Visualization of Linguistic Structures in Large Language Models: Neural Representations of Verb-Particle Constructions in BERT
by Hassane Kissane, Achim Schilling, Patrick Krauss
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study delves into the internal representations of verb-particle combinations within transformer-based large language models (LLMs), examining how these models capture lexical and syntactic nuances at different neural network layers. Employing the BERT architecture, the researchers analyze the representational efficacy of its layers for various verb-particle constructions such as ‘agree on’, ‘come back’, and ‘give up’. The methodology includes dataset preparation from the British National Corpus, followed by extensive model training and output analysis through techniques like multi-dimensional scaling (MDS) and generalized discrimination value (GDV) calculations. Results show that BERT’s middle layers most effectively capture syntactic structures, with significant variability in representational accuracy across different verb categories. These findings challenge the conventional uniformity assumed in neural network processing of linguistic elements and suggest a complex interplay between network architecture and linguistic representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computer models understand language, specifically focusing on verb-particle combinations like ‘agree on’ or ‘give up’. They use a special kind of AI model called BERT to see how it represents different words and phrases. The researchers prepare a big dataset from the British National Corpus and train the model to analyze its outputs. They find that certain layers in the model do better at capturing word order and sentence structure than others, which is surprising because most models are thought to be good at this kind of thing. This research helps us understand how computer models work with language, which can help improve their accuracy and usefulness. |
Keywords
» Artificial intelligence » Bert » Neural network » Transformer
