Temporal question answering is an established method for evaluating temporal reasoning in large language models. Expected answers are often numeric (e.g., dates or durations), yet model responses are evaluated like regular text with exact match (EM), unable to distinguish small from large errors. In this investigative work, we frame temporal question answering as a numerical estimation task to assess the shortcomings of EM. We introduce TempAnswerQA, a benchmark distilled from Test of Time and TempTabQA, where all questions require a numerical, temporal answer, allowing us to evaluate models beyond EM. We use the forecasting metrics symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE). With sMAPE, we find that error size and EM are decoupled. Models with low EM still have low sMAPE (both 20%), and some models have high sMAPE despite high EM. Scaling errors by the deviation of the ground truth data with MASE reshuffles model rankings compared to EM, revealing gaps in models’ understanding of temporal domain knowledge, especially when trained with synthetic data. Lastly, the models’ most frequent error is to deviate by only ±1 from the ground truth. sMAPE and MASE, unlike EM, adequately weight these errors. Our findings underscore the need for specialised metrics for temporal QA tasks. Our code and data are available on https://github.com/aauss/temporal-answer-qa.

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.