Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency

TL;DR

This study evaluates the performance of large language models in zero-shot temporal relation extraction from biomedical clinical notes, highlighting their limitations and analyzing temporal consistency issues.

Contribution

It introduces the first zero-shot approach for biomedical temporal relation extraction and provides a detailed analysis of LLMs' temporal consistency and accuracy.

Findings

LLMs perform worse than fine-tuned models in zero-shot settings.

LLMs struggle with temporal properties like transitivity and uniqueness.

Temporal consistency does not necessarily correlate with prediction accuracy.

Abstract

This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. We employ two types of prompts and five LLMs (GPT-3.5, Mixtral, Llama 2, Gemma, and PMC-LLaMA) to obtain responses about the temporal relations between two events. Our experiments demonstrate that LLMs struggle in the zero-shot setting performing worse than fine-tuned specialized models in terms of F1 score, showing that this is a challenging task for LLMs. We further contribute a novel comprehensive temporal analysis by calculating consistency scores for each LLM. Our findings reveal that LLMs face challenges in providing responses consistent to the temporal properties of uniqueness and transitivity. Moreover, we study the relation between the temporal consistency of an LLM and its accuracy and whether the latter can be improved by solving temporal inconsistencies. Our analysis shows that even when temporal consistency is achieved, the predictions can remain inaccurate.