Context Matters: An Empirical Study of the Impact of Contextual Information in Temporal Question Answering Systems
<|reference_start|>Context Matters: An Empirical Study of the Impact of Contextual Information in Temporal Question Answering Systems: Large language models (LLMs) often struggle with temporal reasoning, crucial for tasks like historical event analysis and time-sensitive information retrieval. Despite advancements, state-of-the-art models falter in handling temporal information, especially when faced with irrelevant or noisy contexts. This paper addresses this gap by empirically examining the robustness of temporal question-answering (TQA) systems trained on various context types, including relevant, irrelevant, slightly altered, and no context. Our findings indicate that training with a mix of these contexts enhances model robustness and accuracy. Additionally, we show that the position of context relative to the question significantly impacts performance, with question-first positioning yielding better results. We introduce two new context-rich TQA datasets, ContextAQA and ContextTQE, and provide comprehensive evaluations and guidelines for training robust TQA models. Our work lays the foundation for developing reliable and context-aware temporal QA systems, with broader implications for enhancing LLM robustness against diverse and potentially adversarial information.<|reference_end|>
@article{schumacher2024context,
    title={Context Matters: An Empirical Study of the Impact of Contextual
  Information in Temporal Question Answering Systems},
    author={Dan Schumacher, Fatemeh Haji, Tara Grey, Niharika Bandlamudi, Nupoor
  Karnik, Gagana Uday Kumar, Jason Cho-Yu Chiang, Paul Rad, Nishant
  Vishwamitra, Anthony Rios},
    journal={arXiv preprint arXiv:2406.19538},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19538},
    primaryClass={cs.CL}
}