Empirical study on temporal expression extraction via large language model
Haolei Wu · 2024
Temporal expression extraction is to identify temporal expressions from raw text. Predominant methods rely on handcrafted rules or high-quality training datasets, and currently few works utilize large language models in this task. This paper demonstrated the ability of large language models to understand temporal information by building few-shot temporal expression extraction system that does not require hand-crafted rules or training. To reduce model output length, the system used phrase-only output schema instead of outputting entire sequence. The model also alleviated hallucination effect by prompting the model to predict expression type and using the model itself to filter model outputs. Experimental results indicated that the model achieved decent performance without training or rules.