Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness

• URL: http://arxiv.org/abs/2406.04156v1
• Date: Thu, 6 Jun 2024 15:17:51 GMT
• Title: Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness
• Authors: Lars Hillebrand, Prabhupad Pradhan, Christian Bauckhage, Rafet Sifa,
• Abstract summary: "pointer-guided segment ordering" (SO) is a novel pre-training technique aimed at enhancing the contextual understanding of paragraph-level text representations. Our experiments show that pointer-guided pre-training significantly enhances the model's ability to understand complex document structures.
• Score: 3.2925222641796554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce "pointer-guided segment ordering" (SO), a novel pre-training technique aimed at enhancing the contextual understanding of paragraph-level text representations in large language models. Our methodology leverages a self-attention-driven pointer network to restore the original sequence of shuffled text segments, addressing the challenge of capturing the structural coherence and contextual dependencies within documents. This pre-training approach is complemented by a fine-tuning methodology that incorporates dynamic sampling, augmenting the diversity of training instances and improving sample efficiency for various downstream applications. We evaluate our method on a diverse set of datasets, demonstrating its efficacy in tasks requiring sequential text classification across scientific literature and financial reporting domains. Our experiments show that pointer-guided pre-training significantly enhances the model's ability to understand complex document structures, leading to state-of-the-art performance in downstream classification tasks.

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