@inproceedings{zhang-etal-2025-power,
    title = "The Power of Simplicity in {LLM}-Based Event Forecasting",
    author = "Zhang, Meiru  and
      Abbood, Auss  and
      Meng, Zaiqiao  and
      Collier, Nigel",
    editor = "Kamalloo, Ehsan  and
      Gontier, Nicolas  and
      Lu, Xing Han  and
      Dziri, Nouha  and
      Murty, Shikhar  and
      Lacoste, Alexandre",
    booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.realm-1.32/",
    pages = "454--470",
    ISBN = "979-8-89176-264-0",
    abstract = "Event forecasting is a challenging task that requires temporal reasoning over historical data. Although iterative reasoning agents following the ReAct paradigm bring improvements to event forecasting tasks, they also increase the cost of each prediction and bring challenges in tracing the information that contributes to the prediction. In this study, we simplify the ReAct framework into a retrieval-augmented generation (RAG) pipeline. Surprisingly, the RAG outperforms ReAct with only 10{\%} of the token costs. Furthermore, our experiments reveal that structured statistical contexts significantly enhance forecasting accuracy, whereas introducing unstructured semantic information (e.g., news article titles) negatively impacts performance. In-depth analyses further highlight that the iterative reasoning traces impair forecasting accuracy in smaller-scale models but benefit larger models (e.g., 70B) in the event forecasting task. These insights underscore existing limitations in large language models' temporal and semantic reasoning abilities, providing critical guidance for developing more cost-effective and reliable forecasting systems."
}