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

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. Figure 1: An example of three event pairs annotated Moreover, we study the relation between the with temporal relations. In the right part, the order of temporal consistency of an LLM and its accuracy the events with respect to time (t) is shown and the and whether the latter can be improved by consistency of uniqueness and transitivity.