Summary of Analysing Zero-shot Temporal Relation Extraction on Clinical Notes Using Temporal Consistency, by Vasiliki Kougia et al.
Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency
by Vasiliki Kougia, Anastasiia Sedova, Andreas Stephan, Klim Zaporojets, Benjamin Roth
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A novel study on temporal relation extraction in biomedical text explores the capabilities of Large Language Models (LLMs) in a zero-shot setting. The research employs five LLMs and two types of prompts to analyze their performance in identifying temporal relations between events. The findings indicate that LLMs struggle in this task, achieving lower F1 scores compared to fine-tuned specialized models. Additionally, the study contributes a comprehensive temporal analysis by calculating consistency scores for each LLM, revealing challenges in providing responses consistent with temporal properties of uniqueness and transitivity. Furthermore, the research examines the relationship between temporal consistency and accuracy, suggesting that even when temporal consistency is achieved, predictions can remain inaccurate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper investigates how large language models (LLMs) work with biomedical text to identify relationships between events happening at different times. The researchers used five different LLMs and tested their ability to understand these relationships without any special training beforehand. They found that the LLMs weren’t very good at this task, which is surprising since they’re often great at understanding language. The study also looked at how well the LLMs did at providing consistent answers about time, and found that even when they got better at that, their answers might still not be accurate. |
Keywords
* Artificial intelligence * Zero shot
