Summary of Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale, by Xiang Hu et al.
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale
by Xiang Hu, Pengyu Ji, Qingyang Zhu, Wei Wu, Kewei Tu
First submitted to arxiv on: 13 Mar 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 A syntactic language model (SLM) is developed to generate sentences incrementally by constructing a syntactic tree from scratch. The Generative Pretrained Structured Transformers (GPST) framework achieves this through two components: a usual SLM with uni-directional language modeling loss and an additional composition model inducing syntactic parse trees with bi-directional language modeling loss. This framework circumvents limitations of previous SLMs, such as reliance on gold trees and sequential training. GPST is pre-trained on OpenWebText, a corpus with 9 billion tokens, demonstrating superiority over GPT-2 in various tasks, including language understanding and generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPST creates sentences by building a syntactic tree from scratch. It’s like having a robot that writes sentences step-by-step! The model has two parts: one that makes words flow together smoothly and another that creates the sentence structure. This helps GPST generate more accurate sentences than other models. By training on a huge text dataset, GPST becomes really good at understanding and creating text. |
Keywords
» Artificial intelligence » Gpt » Language model » Language understanding
