Summary of Graph-dpep: Decomposed Plug and Ensemble Play For Few-shot Document Relation Extraction with Graph-of-thoughts Reasoning, by Tao Zhang et al.
Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning
by Tao Zhang, Ning Yan, Masood Mortazavi, Hoang H. Nguyen, Zhongfen Deng, Philip S. Yu
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 presents a novel approach to document-level relation extraction (DocRE) tasks using large language models (LLMs). The authors address the challenge of converting structured output formats into plain text, which is crucial for few-shot learning. By representing the structured output as a graph-style triplet, they develop the Graph-DPEP framework, comprising three main components: decomposed-plug method for prompt generation, verifier for calibrating generation and identifying overlooked query entity pairs, and ensemble-play to address missingness issues. The proposed framework outperforms existing prompt techniques and alternative LLMs on publicly available benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding relationships between things mentioned in documents using very smart computers called large language models (LLMs). Right now, it’s hard to do this because the output is structured and not easy to understand. The authors came up with a new way to represent this data as a graph, which makes it easier for LLMs to work with. They then developed a system that includes three main parts: one helps generate prompts, another checks if everything is correct, and the last part fills in missing information. By doing this, they were able to make their method better than others. |
Keywords
» Artificial intelligence » Few shot » Prompt
