Summary of Pointer-guided Pre-training: Infusing Large Language Models with Paragraph-level Contextual Awareness, by Lars Hillebrand et al.
Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness
by Lars Hillebrand, Prabhupad Pradhan, Christian Bauckhage, Rafet Sifa
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 novel pre-training technique, “pointer-guided segment ordering” (SO), aims to enhance contextual understanding of paragraph-level text representations in large language models. The self-attention-driven pointer network restores the original sequence of shuffled text segments, addressing challenges in capturing structural coherence and contextual dependencies within documents. This approach is complemented by a fine-tuning methodology incorporating dynamic sampling for increased diversity and improved sample efficiency. Evaluation on diverse datasets demonstrates efficacy in tasks requiring sequential text classification across scientific literature and financial reporting domains. State-of-the-art performance is achieved in downstream classification tasks, highlighting the model’s ability to understand complex document structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help large language models better understand text by putting paragraphs back in their original order. This can be tricky because documents have complex structures that need to be captured. The approach uses a special network that looks at attention to restore the correct sequence of text segments. The method is tested on various tasks and shows it can do well even with smaller training sets. |
Keywords
» Artificial intelligence » Attention » Classification » Fine tuning » Self attention » Text classification
