Summary of Unlocking Instructive In-context Learning with Tabular Prompting For Relational Triple Extraction, by Guozheng Li et al.
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
by Guozheng Li, Wenjun Ke, Peng Wang, Zijie Xu, Ke Ji, Jiajun Liu, Ziyu Shang, Qiqing Luo
First submitted to arxiv on: 21 Feb 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 In this paper, researchers tackle the challenges of designing effective prompts and selecting proper demonstrations for relational triple extraction (RTE), a task that has achieved promising results but still faces limitations. Current methods recast RTE as text-to-text prompting formats, which can lead to mismatched outputs between pre-training and inference times. Additionally, these approaches primarily utilize surface-level natural language features and neglect the importance of triple semantics in sample selection. The authors propose Tabular Prompting for RTE (TableIE), a novel approach that frames the RTE task as a table generation task to incorporate structured information into in-context learning (ICL). This allows for more accurate output conversions to RTE structures. Furthermore, the researchers introduce Instructive I-CL (I^2 CL), which selectively annotates samples based on internal triple semantics, leading to improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper addresses two key challenges in relational triple extraction (RTE) – designing effective prompts and selecting proper demonstrations. Current methods don’t fully address these issues. They often reframe RTE as a text-to-text prompting task, which can result in mismatches between pre-training and inference times for large language models (LLMs). Additionally, they mainly focus on surface-level natural language features and neglect triple semantics in sample selection. This paper proposes new approaches to tackle these challenges. |
Keywords
» Artificial intelligence » Inference » Prompting » Semantics
