Summary of Sail: Sample-centric In-context Learning For Document Information Extraction, by Jinyu Zhang et al.
SAIL: Sample-Centric In-Context Learning for Document Information Extraction
by Jinyu Zhang, Zhiyuan You, Jize Wang, Xinyi Le
First submitted to arxiv on: 22 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 paper proposes a novel approach called Sample-centric In-context Learning (SAIL) for extracting structured information from Visually Rich Documents (VRDs). Unlike previous full-training approaches that excel in specific domains, SAIL leverages pre-trained Large Language Models (LLMs) to tackle various downstream tasks with minimal training. To address the challenges of understanding layout-text relationships and providing accurate guidance to LLMs, SAIL introduces entity-level textual similarity for in-depth text analysis and incorporates layout similarity to enhance VRD analysis. The method also formulates a unified In-Context Learning (ICL) prompt template for various sample-centric examples, allowing tailored prompts that deliver precise guidance to pre-trained models. Experimental results on FUNSD, CORD, and SROIE benchmarks with LLMs as base models demonstrate that SAIL outperforms training-free baselines and approaches the performance of full-training methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to extract important information from documents that have lots of pictures and text. Right now, computers are good at doing this when they’re trained just for that task. But what if we could train them once and then use them on many different types of documents? That’s the idea behind Sample-centric In-context Learning (SAIL). SAIL helps computers understand the relationship between the layout of a document and the text inside it, which is important for extracting information correctly. It also gives the computer guidance on what to look for in each specific document. The results show that SAIL works better than other methods that don’t require as much training. |
Keywords
» Artificial intelligence » Prompt
